diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE200306.csv b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE200306.csv new file mode 100644 index 0000000000000000000000000000000000000000..dd0e38fc0c97c3b0d754c0813df5031c23ad8974 --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE200306.csv @@ -0,0 +1,4 @@ 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b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE112943.py @@ -0,0 +1,162 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE112943" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE112943.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE112943.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# The series title and design indicate microarray gene expression analyses +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# For trait - row 0 contains tissue type indicating disease vs control +trait_row = 0 +# Age and gender not available in sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert tissue type to binary trait""" + if value is None: + return None + value = value.split(": ")[1].strip() + # CCLE and SCLE are lupus cases, others are controls + if value in ['CCLE', 'SCLE', 'Lupus Nephritis']: + return 1 + elif value in ['Skin Control', 'Kidney Control']: + return 0 + return None + +def convert_age(value: str) -> float: + """Convert age to float - not used since age not available""" + return None + +def convert_gender(value: str) -> int: + """Convert gender to binary - not used since gender not available""" + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + print("Preview of extracted clinical features:") + print(preview_df(clinical_features)) + + # Create directories if they don't exist + os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) + + # Save clinical features + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The gene identifiers are Illumina probe IDs (starting with "ILMN_") +# These need to be mapped to human gene symbols for proper analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# 1. From observation, 'ID' in gene annotation matches the probe IDs in gene expression data, +# and 'Symbol' contains the gene symbols + +# 2. Get gene mapping dataframe +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') + +# 3. Convert probe-level to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the result +print("Shape of gene expression data after mapping:", gene_data.shape) +print("\nPreview of gene data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains skin biopsy gene expression data comparing childhood-onset SLE patients with healthy controls." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE131528.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE131528.py new file mode 100644 index 0000000000000000000000000000000000000000..7cc497fdb9425489085fc12995cdec04759e936e --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE131528.py @@ -0,0 +1,170 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE131528" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE131528" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE131528.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE131528.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE131528.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Looking at background info, this appears to be about T1D and biomarkers +# No indication of gene expression data, appears to focus on immune signatures and clinical metrics +is_gene_available = False + +# 2.1 Data Availability +# trait: T1D status is available in row 1 +trait_row = 1 + +# age: Available in rows 3 and 4 in different formats +age_row = 3 + +# gender: Available in rows 4 and 5 +gender_row = 4 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + # Split by colon and get the value + val = x.split(': ')[-1].strip() + if 'T1D' in val: + return 1 + return 0 + +def convert_age(x): + if pd.isna(x): + return None + # Extract numeric value after "age at enrollment:" + val = x.split(': ')[-1].strip() + try: + return float(val) + except: + return None + +def convert_gender(x): + if pd.isna(x): + return None + val = x.split(': ')[-1].strip().lower() + if 'f' in val: + return 0 + elif 'm' in val: + return 1 + return None + +# 3. Save initial metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract clinical features since trait_row is not None +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +preview = preview_df(clinical_features) +print("Preview of clinical features:") +print(preview) + +# Save clinical features +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The identifiers in the gene expression data are Affymetrix probe IDs (e.g. '1007_s_at', '1053_at') +# These need to be mapped to human gene symbols for proper analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Create mapping dataframe from probe IDs to gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') + +# Convert probe-level data to gene-level expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Display info about the result +print("Gene expression data shape after mapping:", gene_data.shape) +print("\nFirst few genes and their expression values:") +print(gene_data.head()) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains skin biopsy gene expression data comparing childhood-onset SLE patients with healthy controls." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE148810.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE148810.py new file mode 100644 index 0000000000000000000000000000000000000000..d4a4f9c6a641d06c64da51dc95a88c7b5b87f5e1 --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE148810.py @@ -0,0 +1,163 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE148810" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE148810.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Series_overall_design mentions "Affymetrix ST 2.1 microarray analysis", so gene expression data is available +is_gene_available = True + +# 2.1 Data Availability +# Trait data (SLE vs control) can be found in disease field (key 1) +trait_row = 1 +# Age and gender data not available in any fields +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + # Extract value after colon + if ':' in value: + value = value.split(':', 1)[1].strip() + # Convert to binary (1 for SLE, 0 for control/normal) + if 'cSLE' in value: + return 1 + elif 'Normal' in value: + return 0 + # Ignore JM samples + return None + +def convert_age(value: str) -> float: + return None # Not used since age data not available + +def convert_gender(value: str) -> int: + return None # Not used since gender data not available + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract Clinical Features +if trait_row is not None: + clinical_features = geo_select_clinical_features(clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + + # Preview the extracted features + preview = preview_df(clinical_features) + print("Preview of clinical features:") + print(preview) + + # Save to CSV + os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The gene identifiers end with "_at" which indicates they are probe IDs from an Affymetrix microarray +# These need to be mapped to human gene symbols for standardization +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# 1. Identify columns for mapping +# 'ID' in annotation corresponds to gene identifiers in expression data +# 'ORF' contains the gene symbols +prob_col = 'ID' +gene_col = 'ORF' + +# 2. Get mapping dataframe +gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Apply mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, gene_mapping) + +# Print shapes to verify the conversion +print("Shape before mapping (probes):", genetic_data.shape) +print("Shape after mapping (genes):", gene_data.shape) + +# Preview first few gene symbols and expression values +print("\nPreview of gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains skin biopsy gene expression data comparing childhood-onset SLE patients with healthy controls." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE154851.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE154851.py new file mode 100644 index 0000000000000000000000000000000000000000..d896518eb205ffa08dbb4e2b54c9a482cf26010c --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE154851.py @@ -0,0 +1,130 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE154851" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE154851" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE154851.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE154851.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE154851.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# The background information mentions using "Human Gene Expression Microarray kit" +# and gene expression analysis, so gene expression data is available +is_gene_available = True + +# 2. Data Availability and Type Conversion +# For trait: Not directly available in characteristics +def convert_trait(x): + if x is None: + return None + value = x.split(": ")[-1].lower() + # Based on series design description - SLE patients vs healthy controls + return 1 if "sle" in value or "lupus" in value else 0 + +# For gender: Available in row 1 +def convert_gender(x): + if x is None: + return None + value = x.split(": ")[-1].lower() + return 1 if "male" in value else 0 + +# For age: Available in row 2 +def convert_age(x): + if x is None: + return None + try: + # Extract numeric value before 'y' + return float(x.split(": ")[-1].replace('y','')) + except: + return None + +# Set row indices based on data availability +trait_row = None # No explicit disease status field +age_row = 2 +gender_row = 1 + +# 3. Save metadata +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False) + +# 4. Skip clinical feature extraction since trait_row is None +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Based on the preview, the gene identifiers are simple numeric values (1, 2, 3, etc) +# These appear to be array probe IDs rather than human gene symbols +# Therefore mapping to gene symbols will be required +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# 1. Identify mapping columns: 'ID' matches probe identifiers, 'GENE_SYMBOL' contains gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL') + +# 2. Map probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Print shape to verify +print("\nGene expression data shape after mapping:") +print(gene_data.shape) +print("\nFirst few genes and their expression values:") +print(gene_data.head()) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# Exit early as trait data is not available +note = "Dataset contains gene expression data but lacks clear trait labels in clinical annotations." +validate_and_save_cohort_info( + is_final=False, # Changed from True to False for early exit + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=None, + df=None, + note=note +) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE165004.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE165004.py new file mode 100644 index 0000000000000000000000000000000000000000..10b9da3b6a697b8c5185c08b17524d4bd6baa21b --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE165004.py @@ -0,0 +1,146 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE165004" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE165004" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE165004.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE165004.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE165004.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this dataset contains gene expression data from endometrial tissue RNA +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Trait, age and gender data availability +trait_row = 0 # "subject status/group" contains trait information about patient vs control +age_row = None # Age information not available +gender_row = None # No gender info needed as all subjects are female + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + """Convert trait values to binary: Control=0, Patient (RPL or UIF)=1""" + if not isinstance(value, str): + return None + value = value.split(': ')[-1].lower() + if 'control' in value: + return 0 + elif 'patient' in value: + return 1 + return None + +convert_age = None +convert_gender = None + +# 3. Save metadata about data availability +validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None)) + +# 4. Extract clinical features since trait_row is not None +selected_clinical_df = geo_select_clinical_features(clinical_data, trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + +# Preview the clinical data +print(preview_df(selected_clinical_df)) + +# Save clinical data +selected_clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The gene identifiers in this dataset are numeric IDs. These do not appear to be human gene symbols +# and will need to be mapped to standard symbols for biological interpretation. +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Get gene mapping from annotation data +# ID column contains probe identifiers matching gene expression data +# GENE_SYMBOL column contains the target gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL') + +# Apply gene mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) + +# Preview results +print("\nGene expression data shape after mapping:", gene_data.shape) +print("\nFirst few gene symbols and their expression values:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset appears to be from endometrial tissue RNA expression study comparing patients with recurrent pregnancy losses and unexplained infertility vs controls." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180393.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180393.py new file mode 100644 index 0000000000000000000000000000000000000000..0fc10598a972db01657ff9c2c0117fc4dd5ea844 --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180393.py @@ -0,0 +1,196 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE180393" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE180393" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE180393.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE180393.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE180393.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Based on the background information mentioning microarray profiling on Affymetrix ST2.1 platform +is_gene_available = True + +# 2.1 Data Availability +# From Sample Characteristics: +# - Trait (SLE status) can be inferred from sample group field (row 0) which shows LN vs non-LN cases +trait_row = 0 +# Age and gender are not available in the sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert sample group to binary SLE status (0: non-SLE, 1: SLE)""" + if not isinstance(value, str): + return None + value = value.split(': ')[-1].strip() + # LN (Lupus Nephritis) cases indicate SLE + if value.startswith('LN-WHO'): + return 1 + # All other cases are non-SLE controls + elif value in ['Living donor', 'infection-associated GN', 'FSGS', 'DN', + 'amyloidosis', 'Membrano-Proliferative GN', 'MN', 'AKI', + 'FGGS', "2'FSGS", 'Thin-BMD', 'Immuncomplex GN', 'IgAN', + 'chronic Glomerulonephritis (GN) with infiltration by CLL', + 'CKD with mod-severe Interstitial fibrosis', 'Fibrillary GN', + 'Interstitial nephritis', 'Hypertensive Nephrosclerosis', + 'Unaffected parts of Tumor Nephrectomy']: + return 0 + return None + +convert_age = None +convert_gender = None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Extract Clinical Features +if trait_row is not None: + selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + print("Preview of selected clinical features:") + print(preview_df(selected_clinical)) + + # Save to CSV + selected_clinical.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs (gene/probe identifiers) +print("First 20 gene/probe IDs:") +print(list(genetic_data.index)[:20]) +# Looking at the identifiers which end in '_at', these appear to be Affymetrix probe IDs +# rather than standard human gene symbols. These will need mapping. +requires_gene_mapping = True +# Extract gene annotation data - use fewer prefixes to avoid filtering out annotation data +annotation_data = filter_content_by_prefix(soft_file_path, prefixes_a=['!'], unselect=True, source_type='file', return_df_a=True)[0] + +# Print all column names first +print("All columns in annotation data:") +print(annotation_data.columns.tolist()) + +# Preview column names and first few values +print("\nGene annotation preview:") +print(preview_df(annotation_data)) +# Extract gene annotation data +annotation_data = get_gene_annotation(soft_file_path) + +# Print all column names +print("All columns in annotation data:") +print(annotation_data.columns.tolist()) + +# Preview column names and first few values +print("\nGene annotation preview:") +print(preview_df(annotation_data)) +# Extract gene mapping from annotation data (probe ID to Entrez ID) +mapping_df = get_gene_mapping(annotation_data, prob_col='ID', gene_col='ENTREZ_GENE_ID') + +# Load NCBI gene ID to symbol mapping +with open("./metadata/gene_id_to_symbol.json", "r") as f: + entrez_to_symbol = json.load(f) + +# Add gene symbol column by mapping Entrez IDs +mapping_df['Gene'] = mapping_df['Gene'].map(lambda x: entrez_to_symbol.get(x)) + +# Drop rows where gene symbol mapping failed +mapping_df = mapping_df.dropna() + +# Apply gene mapping to convert probe expression to gene expression +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the first few gene IDs and values +print("\nFirst 20 genes after mapping:") +print(list(gene_data.index)[:20]) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# Drop rows where gene symbol mapping failed +genetic_data = get_genetic_data(matrix_file_path) +mapping_df = get_gene_mapping(annotation_data, prob_col='ID', gene_col='ENTREZ_GENE_ID') + +# Debug gene mapping process +print("Number of probes in mapping:", len(mapping_df)) +print("Number of probes in gene data:", len(genetic_data)) +print("Number of overlapping probes:", len(set(mapping_df['ID']).intersection(genetic_data.index))) + +gene_data = apply_gene_mapping(genetic_data, mapping_df) +print("Shape of gene data after mapping:", gene_data.shape) +print("First few genes after mapping:", list(gene_data.index)[:5]) + +# 1. Normalize gene symbols using NCBI data +gene_data = normalize_gene_symbols_in_index(gene_data) +print("Shape of gene data after normalization:", gene_data.shape) +print("First few normalized genes:", list(gene_data.index)[:5]) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0) +print("\nShape of clinical data:", selected_clinical.shape) +print("Clinical data index:", selected_clinical.index.tolist()) + +linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data) +print("Shape of linked data:", linked_data.shape) +print("Linked data columns:", linked_data.columns[:5].tolist()) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) +print("Shape after handling missing values:", linked_data.shape) + +# 4. Check for biased features +is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Save validation info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available, + is_biased=is_trait_biased, + df=linked_data, + note="Dataset uses microarray probes that were mapped to genes. Age and gender info not available." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180394.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180394.py new file mode 100644 index 0000000000000000000000000000000000000000..766d5ab277cac5703b4a52bcd989006d716578ef --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180394.py @@ -0,0 +1,129 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE180394" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE180394" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE180394.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE180394.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE180394.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Step 1: Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Step 2: Extract background info and clinical data from matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Step 3: Get dictionary of unique values for each clinical feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Step 4: Print background info and sample characteristics +print("Dataset Background Information:") +print("-" * 80) +print(background_info) +print("\nSample Characteristics:") +print("-" * 80) +print(json.dumps(unique_values_dict, indent=2)) +# 1. Gene Expression Data Availability +# Based on the background info, this dataset uses Affymetrix microarray for gene expression profiling +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Trait (Lupus/SLE) can be determined from sample group in row 0 +trait_row = 0 +# Age and gender info not available in characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert trait value to binary (0: control, 1: lupus)""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().lower() + # Living donor and tumor nephrectomy are controls + if 'living donor' in value or 'tumor nephrectomy' in value: + return 0 + # LN = Lupus Nephritis cases + elif 'ln-who' in value: + return 1 + return None + +def convert_age(value: str) -> Optional[float]: + """Convert age value to float""" + return None # Not used since age data not available + +def convert_gender(value: str) -> Optional[int]: + """Convert gender to binary (0: female, 1: male)""" + return None # Not used since gender data not available + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + print("Preview of clinical features:") + print(preview_df(clinical_features)) + clinical_features.to_csv(out_clinical_data_file) +# 1. Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# 2. Print first 20 row IDs +print("First 20 gene/probe identifiers:") +print(genetic_data.index[:20]) +# Gene identifiers contain "_at" suffix which indicates they are probe IDs from the Affymetrix platform +# These need to be mapped to human gene symbols for analysis +requires_gene_mapping = True +# 1. Extract gene annotation data from SOFT file +# Try to get gene symbols from platform table section +gene_annotation = None +with gzip.open(soft_file_path, 'rt') as f: + platform_start = False + header = None + data = [] + for line in f: + if '!Platform_table_begin' in line: + platform_start = True + continue + elif '!Platform_table_end' in line: + break + elif platform_start: + if header is None: + header = line.strip().split('\t') + else: + data.append(line.strip().split('\t')) + +if data: + gene_annotation = pd.DataFrame(data, columns=header) + +# 2. Preview annotation data columns and check for gene symbol info +if gene_annotation is not None: + print("Available columns in annotation data:") + print(gene_annotation.columns) + print("\nSample of annotation data:") + print(preview_df(gene_annotation)) +else: + print("No platform table data found in SOFT file") \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE184989.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE184989.py new file mode 100644 index 0000000000000000000000000000000000000000..21b531904f686e076d77162771be13139b9167ab --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE184989.py @@ -0,0 +1,158 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE184989" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE184989" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE184989.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE184989.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE184989.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Step 1: Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Step 2: Extract background info and clinical data from matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Step 3: Get dictionary of unique values for each clinical feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Step 4: Print background info and sample characteristics +print("Dataset Background Information:") +print("-" * 80) +print(background_info) +print("\nSample Characteristics:") +print("-" * 80) +print(json.dumps(unique_values_dict, indent=2)) +# 1. Gene Expression Data Availability +# Yes, based on the background info mentioning "microarray experiment" and "gene expressions" +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Disease state is in row 0, with multiple values including ACLE, SCLE, DLE vs NN (normal) +trait_row = 0 + +# Age and gender not available in characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + x = x.split(": ")[-1].strip().upper() + # Convert to binary - any lupus type vs normal + if x == "NN": + return 0 + elif x in ["DLE", "SCLE", "ACLE"]: + return 1 + return None + +def convert_age(x): + return None # Not used since age not available + +def convert_gender(x): + return None # Not used since gender not available + +# 3. Save initial metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract clinical features +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +print("Preview of clinical features:") +print(preview_df(clinical_df)) + +# Save to CSV +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# 1. Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# 2. Print first 20 row IDs +print("First 20 gene/probe identifiers:") +print(genetic_data.index[:20]) +# These appear to be microarray probe IDs, not human gene symbols +# The format '16650XXX' is characteristic of probe IDs that need to be mapped to gene symbols +requires_gene_mapping = True +# 1. Extract gene annotation data from SOFT file +gene_annotation = get_gene_annotation(soft_file_path) + +# 2. Preview annotation data +print("Column names and first few values in gene annotation data:") +print(preview_df(gene_annotation)) + +# Preview additional rows to check for gene annotations +print("\nPreview of rows 100-105:") +print(preview_df(gene_annotation.iloc[100:105])) +# 1. Get gene mapping +# Looking at outputs, we can see: +# - gene identifiers in gene expression data are numeric strings like '16650001' +# - 'ID' column in gene annotation data matches these identifiers +# - 'gene_assignment' column contains gene symbols +mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment') + +# 2. Apply gene mapping to convert probe data to gene data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview results +print("\nFirst few rows of gene_data:") +print(preview_df(gene_data)) + +# Save intermediate gene data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols and save gene data +normalized_gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = pd.concat([clinical_df, normalized_gene_data], axis=0).T + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove biased demographic ones +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains gene expression data from kidney biopsies of lupus nephritis patients. Trait is based on treatment response (responders vs non-responders)." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE193442.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE193442.py new file mode 100644 index 0000000000000000000000000000000000000000..0b71f8c2dba28e0ca16bdac7ea62965b2eb79d01 --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE193442.py @@ -0,0 +1,76 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE193442" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE193442.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE193442.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + + +# Step 1: Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Step 2: Extract background info and clinical data from matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Step 3: Get dictionary of unique values for each clinical feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Step 4: Print background info and sample characteristics +print("Dataset Background Information:") +print("-" * 80) +print(background_info) +print("\nSample Characteristics:") +print("-" * 80) +print(json.dumps(unique_values_dict, indent=2)) +# 1. Gene Expression Data Availability +# Given this is a SuperSeries with unclear SubSeries content, +# we should be conservative about data availability +is_gene_available = False + +# 2. Variable Availability and Data Type Conversion +# Looking at the sample characteristics, no trait/disease status, age or gender information is recorded +trait_row = None +age_row = None +gender_row = None + +# Define conversion functions even though not needed for this dataset +def convert_trait(x): + return None + +def convert_age(x): + return None + +def convert_gender(x): + return None + +# 3. Save metadata and conduct initial filtering +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Skip since trait_row is None, indicating no clinical data available +# 1. Print SuperSeries detection info +print("Dataset Info:") +print("-" * 80) +with gzip.open(matrix_file_path, 'rt') as file: + for line in file: + if "!Series_type" in line or "!Series_summary" in line: + print(line.strip()) + +print("\nNOTE: This is a SuperSeries without direct gene expression data.") +print("Gene expression data extraction skipped as is_gene_available was already set to False.") \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE200306.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE200306.py new file mode 100644 index 0000000000000000000000000000000000000000..7d22c1a3d6674e724a5375a8e653ae342e978e9b --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE200306.py @@ -0,0 +1,141 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" +cohort = "GSE200306" + +# Input paths +in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)" +in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE200306" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE200306.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE200306.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE200306.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Step 1: Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Step 2: Extract background info and clinical data from matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Step 3: Get dictionary of unique values for each clinical feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Step 4: Print background info and sample characteristics +print("Dataset Background Information:") +print("-" * 80) +print(background_info) +print("\nSample Characteristics:") +print("-" * 80) +print(json.dumps(unique_values_dict, indent=2)) +# 1. Gene Expression Data Availability +is_gene_available = True # Based on Series info mentioning nanostring analysis of immune transcripts + +# 2. Variable Availability and Data Type Conversion + +# 2.1 Data Availability +trait_row = 5 # 'clinical response group' contains trait info +age_row = 2 # 'age (yrs)' contains age info +gender_row = 1 # 'Sex' contains gender info + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + if ':' in x: + val = x.split(':')[1].strip().lower() + if 'no response' in val: + return 1 # Non-responders + elif 'complete response' in val or 'partial response' in val: + return 0 # Responders + return None + +def convert_age(x): + if pd.isna(x): + return None + if ':' in x: + try: + return float(x.split(':')[1].strip()) + except: + return None + return None + +def convert_gender(x): + if pd.isna(x): + return None + if ':' in x: + val = x.split(':')[1].strip().upper() + if val == 'F': + return 0 + elif val == 'M': + return 1 + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +_ = validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + print("Preview of extracted clinical features:") + print(preview_df(clinical_features)) + + # Save clinical data + os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) + clinical_features.to_csv(out_clinical_data_file) +# 1. Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# 2. Print first 20 row IDs +print("First 20 gene/probe identifiers:") +print(genetic_data.index[:20]) +# The gene identifiers appear to be standard human gene symbols (HGNC symbols) +# They match well-known gene names like ABCB1, ATM, B2M, etc. +# No mapping to other identifiers is needed +requires_gene_mapping = False +# 1. Normalize gene symbols and save gene data +normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove biased demographic ones +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains gene expression data from kidney biopsies of lupus nephritis patients. Trait is based on treatment response (responders vs non-responders)." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/TCGA.py b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..100241b7511e2575ec4db3013cdd59d77081fbff --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/TCGA.py @@ -0,0 +1,25 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Lupus_(Systemic_Lupus_Erythematosus)" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json" + +# Review subdirectories and check if any matches our target trait (Lupus) +# Since none of the directories match lupus or related terms, we need to mark task as completed +# Validate and save this information in json file +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=False, + is_trait_available=False +) \ No newline at end of file diff --git a/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv new file mode 100644 index 0000000000000000000000000000000000000000..4ec339f3d9642805478762be55ecd987767ccd0e --- /dev/null +++ b/p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv @@ -0,0 +1,22857 @@ 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+ATP13A2,4.368849612,3.541648256,4.024122503,4.258313714,4.123508432,4.140152981,3.581528382,4.129626723,3.490746534,4.045007195,4.471198912,3.840106049,4.322217771,4.431586775,4.136451087,4.818415678,4.386576025,4.741137141,4.157523541,4.192805957,3.890704204,4.109769416,3.893757948,3.40565707,3.958997841,3.379910228,3.983760652,3.531422265,4.065807767,3.938639439 +ATP13A3,6.776989573,6.100727811,5.594630744,5.229618778,6.038851587,5.925502706,5.22916693,6.0280727,4.349927259,4.209014277,5.669065066,4.458197074,4.225557484,5.245956007,6.136070274,6.050911694,6.122272697,6.000105229,6.277882439,5.203985777,6.10447987,5.99464388,4.316999521,5.843302694,5.38003328,5.692525403,5.026229271,5.560717061,5.511785954,4.623626415 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+ATP1A1,7.71399938,7.344654659,6.641722336,6.543716701,7.29485672,6.882359192,7.043686143,7.653845443,5.348197789,6.690995295,6.794481027,6.255948803,5.859124697,7.415005439,6.717880949,7.057272434,6.485660424,7.062786561,7.469788275,6.623044735,6.801522812,7.209424176,5.948782338,7.151292199,7.209178255,7.289113854,6.368508455,6.908008612,6.286274069,6.695217237 +ATP1A1-AS1,5.692127907,5.445096753,5.856754075,5.795954718,5.771133843,5.540046325,5.278286004,6.476469431,3.932497572,6.231995315,5.129577907,5.655268601,3.756942055,6.464898106,5.328166759,5.476718422,5.43784852,5.193842545,5.880056079,5.491331654,5.383411813,5.431045082,4.404967141,5.554102082,5.244017552,5.823948485,5.290026869,4.792899793,3.860600347,5.468916402 +ATP1A2,2.855599454,2.654912638,3.859507009,3.718956252,3.859507009,3.610689421,3.910213943,4.131791298,4.884762578,5.019565557,3.859507009,4.332572493,5.195913063,5.162312081,4.004651866,2.912531645,3.083566193,3.859507009,3.937720453,4.063054998,3.134563107,4.372259591,4.142561547,4.473687167,4.373403052,3.987943862,3.614638963,2.89658382,3.649541024,3.859507009 +ATP1A3,4.373340443,2.610445981,4.210255856,4.430158298,4.241147778,3.85510352,3.149165391,3.748223527,5.229858104,4.764242633,3.490378751,4.483027641,5.79139557,5.095757161,4.779764722,5.403586032,4.521878462,3.408436646,3.702077081,4.754749056,4.609133346,2.866537682,3.436531261,3.302185293,3.451876909,3.038856793,4.733146468,2.843858529,4.060167112,3.830147984 +ATP1A4,2.986675809,3.284017763,4.349142041,3.72028527,2.991164015,3.041065602,2.931355071,3.92281241,4.933053491,3.260452216,3.426458182,3.651413208,4.754599233,4.075224043,4.332705692,2.999207763,4.448107642,3.344736151,3.539119565,3.259124414,4.018860926,3.099239857,3.82555545,3.49570853,3.932034712,3.368475069,3.279965887,2.892433764,3.913403009,3.285230917 +ATP1B1,7.63929093,6.630393489,5.39708428,6.376425716,7.610073388,6.972392749,6.668241493,8.377819723,5.564939625,6.738187289,5.23815559,4.871928061,4.123735695,6.293866642,6.464868861,6.845711158,7.751579993,6.627627363,7.382919685,6.014643064,5.673076833,6.988587148,5.725259629,7.219762904,7.495390173,8.468675402,5.587657679,7.442938302,5.92023416,7.792453447 +ATP1B2,5.236801264,5.662237731,5.157827116,5.578642782,5.271815815,5.824314502,5.271815815,5.137798082,5.402698414,5.575699178,5.425244723,5.184167095,5.401559664,5.271815815,5.265690906,4.843503381,4.621887712,5.608048055,5.271815815,4.948946715,4.646255873,5.570597926,5.366667452,5.614225039,5.491443902,5.218410025,5.271815815,5.098480564,5.013272308,5.67724496 +ATP1B3,8.108327551,7.704160712,6.952368378,6.796143206,8.516170547,7.483463647,7.649478047,7.88696188,4.903195681,6.667660507,6.401396025,5.445765688,5.176976848,6.936713409,5.571262598,6.849640148,6.482831928,7.579591503,7.848072689,7.289337478,6.875480847,7.341864424,5.653564223,7.919030342,7.339863723,7.436179425,7.823968924,7.249098832,6.989405288,6.949038156 +ATP1B4,2.916215326,3.848614081,3.571184007,3.606310514,3.065939759,3.097468858,2.885052866,3.088268819,3.627711936,3.65788278,4.162951339,3.306581866,4.14085139,3.356582739,3.230417612,3.34415334,3.548762407,3.257257544,3.266725983,3.900039007,3.681184534,4.100974898,3.604659882,3.128833565,3.551461369,3.786171688,4.280590541,3.25098478,3.67960062,4.119785481 +ATP23,4.268213634,3.822094169,2.836798733,3.894920697,4.637291987,4.745454818,4.891847882,4.592228095,4.181278963,4.510803884,3.57809824,3.424575967,3.037655304,2.576332296,3.738612612,4.214495073,3.655954743,3.763955452,3.960379403,4.211030394,3.822430678,4.103834082,4.526183032,4.904058283,5.79604853,4.20562836,3.597973775,4.04397954,4.741957106,3.904506947 +ATP2A1,4.333792909,4.931960797,4.314074873,4.928390995,4.89158491,3.994840003,4.207263721,4.258935991,5.748265755,4.247424956,4.749088517,5.21281932,5.306996046,4.977813547,5.217118435,4.787308249,4.773884809,4.834361579,5.040142311,4.453995572,4.786546024,5.153919525,5.107483599,5.067850481,4.187013048,4.6434565,5.290275578,5.363878803,4.790112055,5.441094315 +ATP2A1-AS1,4.035810156,3.270191091,3.993562637,5.010100605,4.221933206,3.287806465,4.094406626,3.744050464,4.778552436,4.033692213,4.340867613,3.646857156,4.544550177,4.207507842,4.035914564,4.24967546,4.51876479,3.616580652,3.651540381,4.762457043,3.824782726,3.921798826,4.140992573,4.858200268,4.265545707,4.235600646,4.264535094,5.21277957,3.634561497,3.804272785 +ATP2A2,8.187936891,7.856566448,7.389238031,7.676422834,7.56062229,7.854634493,7.563972679,7.618700111,6.629719293,6.770627404,7.043166125,7.355332889,7.491184598,7.481050335,7.407952692,7.588173124,7.453130806,7.870257925,7.576639926,7.727669806,7.472700794,7.969431001,6.414041042,7.518723037,7.649859358,7.848345787,6.883592768,7.281085641,7.169252564,7.099394842 +ATP2A3,5.20810308,5.284683248,3.841637617,6.09332859,4.969407442,4.661276217,4.796252086,5.000242481,3.963402485,6.156702379,3.933290617,6.026831754,4.848000643,5.157942785,4.622847472,5.486251865,4.377935605,5.681132677,6.109139841,4.612717292,5.927819866,5.190325457,4.162946134,4.786017537,5.240989548,4.870154115,5.367894199,4.979407413,5.217198819,5.280723489 +ATP2B1,7.793766229,7.135803235,6.400185733,5.324969363,7.45647885,6.800722294,6.921105586,6.437819138,3.901419541,4.664580198,5.234842249,6.317576136,4.612897736,5.733848216,4.989127573,6.053362477,6.605949997,6.964480914,5.997131808,6.401598618,7.024164226,7.598369779,5.373974612,7.081491015,6.943772497,6.839524392,5.544393749,6.310077956,5.804443994,5.054682568 +ATP2B1-AS1,5.14821611,5.779925658,5.472177101,5.370829772,5.664857451,5.540454542,5.534673528,5.007765538,4.735911414,5.244312429,4.711167504,5.576607083,5.892440152,5.130795165,4.69671904,4.98626296,5.191700149,5.145133909,5.614451009,4.986144613,5.298384074,5.626809096,5.480648912,5.425165445,5.374798808,5.236638142,6.26081942,4.777745891,6.16916508,4.926984206 +ATP2B2,3.405660561,2.991727198,4.542333041,4.062335651,3.126200824,2.792224298,3.115421228,2.648984236,3.720958623,3.872803378,2.915085774,4.184361223,4.386487159,3.873470084,4.197833168,2.697377544,2.548130018,3.105643683,3.259793502,3.025599589,3.198098648,3.413829293,3.702733413,3.020605436,3.409804082,3.186311904,3.998823179,3.427264795,3.545906867,3.651252701 +ATP2B2-IT2,3.659390267,3.464424089,4.464981943,3.602691522,3.17367167,3.333587315,3.180967395,3.639973404,5.546255186,4.305146905,3.345893992,4.507201935,4.352464872,4.285297787,4.579067806,3.698457699,4.026344958,3.255689687,3.257868725,3.425887205,3.472341005,3.799428246,4.613003895,3.651311303,2.700978888,4.214885353,4.806393429,3.244552107,3.267114642,4.396323778 +ATP2B3,3.021099074,2.462870243,3.041727073,2.067683459,3.349046458,3.768952782,2.811413001,2.180788633,4.531574026,3.163161653,4.023992586,3.786315505,3.671236444,3.945588547,4.219220379,2.30446676,2.927931938,2.133546017,2.905651898,2.252717966,2.396451221,2.10659495,4.415283567,2.719573858,2.146477736,1.985805175,3.095316794,3.924643673,4.0620767,3.414545296 +ATP2B4,8.107250213,7.523934723,7.91067538,6.937118736,8.453669277,7.994939185,7.508578482,7.464084027,5.865767208,7.134204419,6.803580071,7.110705462,6.227111199,7.164405837,6.683989032,7.29259221,6.999851859,7.400044942,6.895203536,7.817500487,7.541390334,8.133975347,6.872147853,7.887370721,8.187459218,7.707356715,6.428566915,7.365235832,6.965560692,7.482069879 +ATP2C1,5.878351105,6.132241766,5.649162004,5.866800891,5.619590976,5.823124942,6.321623929,6.290735153,5.344056166,4.939839636,5.759795491,5.563862003,4.878700702,5.677882201,4.996376567,6.391097071,5.890859881,6.262977496,5.925402091,6.146778824,6.066100918,6.080672217,5.038356122,6.141186437,5.956841203,5.901486767,5.433816075,5.634891446,5.511850382,5.486118519 +ATP2C2,5.329554251,6.271802999,5.711523935,5.575691555,5.199845802,4.396909426,6.077550731,5.155224915,5.081222862,3.964911907,4.935175059,4.656435593,3.710774086,4.554083876,4.135355142,5.214699159,4.569573967,4.9622498,4.766464787,4.924413002,4.256971943,4.740061771,4.825276991,4.924413002,5.569417576,4.901628536,4.53367743,4.296282244,4.764720112,4.436472062 +ATP4A,3.003408126,3.826865335,4.15372143,3.395567396,3.886199979,3.053661404,3.818177177,3.890669757,4.10940919,4.412101558,3.672986562,3.471822781,4.468398184,4.572852417,4.491166983,3.375963357,2.952950321,3.922295118,3.856131078,4.170968497,3.657734833,3.11293394,4.120056409,3.615823923,3.801404276,3.811673342,3.878406025,4.665168384,3.88950248,3.632222123 +ATP4B,4.621677455,4.22089088,4.448492662,4.04861975,4.665897296,4.385058695,3.644552779,3.711721738,4.359086656,4.885175879,3.914845517,4.734495801,4.192723253,4.983125351,4.377801228,3.991489177,4.059603872,3.547730007,4.010592027,4.526297817,4.023754519,4.361932154,5.031252962,4.411427719,4.218449148,4.181351158,3.876993813,4.361932154,4.361932154,5.025074006 +ATP5F1A,7.736275293,7.541807123,7.212125788,7.261493742,7.612817044,7.499107231,7.81634252,7.490481351,5.65624486,5.901491569,6.737655068,6.571638807,6.127616861,7.244053205,6.253753218,7.714392877,7.592986066,7.65850326,7.545771414,7.327946556,7.399666973,7.667167534,6.76280431,7.844558477,7.927921088,8.425897537,7.391397843,8.296718841,7.425896063,6.905572425 +ATP5F1B,8.134297506,7.31651939,6.726525637,5.896735177,7.790258381,7.207236296,7.429460266,7.555661504,6.279162956,6.307312921,6.078582208,6.47699929,4.833480076,6.341087573,6.080652477,7.554593689,7.134216828,7.053479002,7.294971805,7.285298925,7.110556832,7.266767574,6.539630032,6.962581586,7.221439915,7.645113246,6.186513252,7.273027787,6.227857729,6.617393735 +ATP5F1C,7.290093118,7.455563866,7.089726331,6.752338826,7.632468389,7.648409456,7.564959983,8.027072761,6.597091986,5.90737019,5.965307591,6.754105642,5.603494158,6.492224761,6.4057286,7.050333864,7.46979184,7.077304879,7.036971565,7.12543215,7.136783206,6.984478781,6.253230196,7.476259836,7.509897341,7.755455914,7.021688165,7.485196873,6.720692356,6.343396018 +ATP5F1D,6.277304841,5.959627426,5.616645968,5.438556398,6.369937202,6.121095181,6.493580656,7.316503313,5.575425231,5.467931334,5.880170644,5.281962897,4.576130496,5.722434649,5.421240113,6.243185658,6.303518932,6.610551406,6.036437082,6.441961586,6.287751414,5.919182388,5.46807221,6.395849963,6.799633283,7.45718985,5.94866797,6.010225858,5.550748094,5.144567226 +ATP5F1EP2,2.49797515,3.004160416,2.380649679,2.021541476,2.188431152,1.699913961,2.14642184,2.15734901,2.448386731,2.799685928,1.904216376,2.462165663,5.109023686,3.519231078,1.70868519,1.660117057,1.888183832,1.625405514,2.042317705,2.060041514,1.78255929,1.982106286,2.202613353,2.521552505,2.595240611,2.733469291,3.018792108,1.767549128,2.19288795,3.224770158 +ATP5IF1,6.015895737,6.141423282,5.747876699,5.288248245,6.514830684,6.102497366,6.390392285,6.828377602,5.638575321,5.526639362,5.090641381,5.624055428,6.102996716,5.822029849,5.177325218,6.180754464,6.033035017,5.813097585,5.55640919,5.837182984,5.396059191,5.950857439,5.697323939,6.037476313,6.235630965,6.067754937,5.861158475,5.609184391,5.886788679,5.747458315 +ATP5MC1,5.295640029,4.49109251,6.009985661,3.504885713,6.078641287,4.545325797,4.869677267,5.730020512,4.560499903,3.554911518,4.388969077,4.567951896,4.581088497,2.945327761,5.50811336,5.286209342,4.889530657,4.357536142,4.581088497,4.406937614,3.916929845,4.47773251,4.09743225,5.460374973,6.301401418,4.837915769,4.301521072,5.355167541,4.581088497,4.783370803 +ATP5MC2,5.416781205,4.415904022,5.318645394,5.389378925,5.474488059,5.292452264,4.726569844,5.604152353,6.304332855,5.514824336,5.890386185,5.068581543,5.392358651,4.012889882,4.703091619,4.971463375,5.086446698,4.442486807,5.07745528,5.177583514,5.317736684,5.108412235,6.173904685,5.730810931,5.248711822,5.651514676,4.928357989,4.968901579,5.200787926,4.96256403 +ATP5MC3,7.348469997,6.646599777,6.514857977,5.981327755,7.07069956,6.989387139,6.832135369,6.691821617,6.891735854,6.520236865,6.105159321,5.769381536,5.325503894,5.120622811,6.1123685,6.894117086,7.003812171,6.833388823,6.916885426,6.572511393,6.796214655,6.996553242,6.000586409,6.926503084,6.513708769,7.502767537,6.040703974,7.362835332,5.770335845,5.743963557 +ATP5ME,7.418165912,6.243778316,5.83484112,5.914874007,8.744531046,7.885092523,7.999369625,8.55306927,6.680476637,6.584993249,6.637121065,6.414369672,5.881944811,5.729141639,6.65746464,6.873348395,7.245367388,6.926161952,6.850213551,6.8432941,7.111743534,7.078042987,5.925496679,7.86785208,8.479918781,8.704214879,6.991588914,7.949927881,7.067319073,6.732950771 +ATP5MF,10.86292765,10.55084142,10.33926908,10.55535562,10.51477749,10.61619761,10.66916009,10.77309688,10.12470626,8.940593482,10.39951712,9.377690204,10.21639435,9.979217897,10.09184876,10.80608625,10.82230166,10.47473069,10.6781504,10.78467108,10.8332664,10.54800008,10.28017665,11.08243436,10.97223605,11.06933025,10.81625013,11.18042287,10.71508282,11.08832921 +ATP5MG,5.674376118,5.713119319,4.670960139,5.639273159,6.514886335,5.599471016,4.416180502,5.601978972,6.414978465,5.652318361,5.815989284,5.186348203,4.959961044,5.815989284,6.737701084,6.220014297,6.037681,5.379969781,5.60696374,5.505806582,6.188067947,6.059598,6.951857526,6.015796162,5.555175069,5.936265419,5.981271597,6.717444645,6.102169736,5.69213792 +ATP5MGL,4.156847893,3.829520809,4.20080876,4.334174734,4.753010998,4.136476167,4.167250946,4.403117448,4.412565941,4.38424259,4.078251006,4.601663872,3.694585516,4.982480816,3.335568379,3.473440336,5.01735842,4.023261818,3.405694262,4.557552597,4.602114543,4.034302024,3.987366477,3.415293312,4.331865066,4.622579773,4.654852367,3.940541356,4.05492933,4.855550635 +ATP5MJ,7.687138463,6.595963816,7.028546082,5.938095452,8.280246159,7.399420639,7.637745375,8.285125248,5.334175629,5.578810978,6.51180208,6.208522844,5.440630037,6.176645209,5.977639625,7.628569668,7.687930676,6.719749159,6.978586732,7.166405808,7.396023632,7.016705976,7.325151029,7.509868354,7.289425634,8.520745467,7.069297396,7.980591376,7.317549163,6.891987903 +ATP5MK,8.449651841,7.781422775,7.596907982,6.908842506,9.426567184,9.457550145,8.208480805,8.926584785,6.436666262,6.511820061,6.958390019,6.039759265,5.734926945,6.095323423,6.073309836,8.107283501,8.468086403,7.829035914,7.751071597,7.49480258,8.038389557,7.90951267,6.555681161,8.407908609,8.519295418,9.387738899,7.538990052,8.847128077,8.385450752,6.546939571 +ATP5PB,7.589791076,6.881307145,6.28457355,6.490186445,7.780964669,7.509705655,6.733496706,7.726777797,6.639210861,6.110390576,6.270588882,6.323751167,5.843933436,6.69636642,6.327690895,6.66203398,6.996962611,7.244290599,6.444643988,6.619083853,6.825056765,7.011772485,5.978971391,7.266875042,7.449574874,8.279906218,6.413940464,7.103411398,6.142337234,6.397534371 +ATP5PBP7,3.881809702,4.159131552,2.866548041,4.011860434,3.385544431,2.074323673,3.82452193,4.270628305,3.426656823,3.883138656,2.656181714,4.25159081,4.224105128,4.127869574,3.21975868,2.689298219,4.042934674,3.858900577,2.325185962,4.069531595,2.933058817,4.001749462,3.325761266,2.177295966,2.414443275,3.325761266,3.625088592,2.364315786,3.325761266,3.721384417 +ATP5PD,5.142927221,4.469214226,4.625917669,4.220930806,5.99400857,5.368699901,4.030086644,5.077233497,3.879814255,2.315412669,5.036499637,4.774545711,2.456330765,2.940492351,3.000312859,3.89725679,4.000420997,4.842139124,3.456925331,3.672404123,3.860018328,4.316559223,3.397956471,4.702297446,5.848775106,5.813184709,3.753782494,4.825262473,3.579711718,4.87260732 +ATP5PF,6.365073971,6.184478466,5.879432947,5.940785976,6.547087472,6.594054029,6.419239635,6.406381707,5.082740626,5.877259425,6.699535381,6.558143083,5.652220899,5.42705114,6.634210891,6.701476608,6.316787821,6.168116206,6.577663863,6.093549355,6.137862851,6.265238731,6.275136882,5.543150647,6.39544756,6.217269101,5.847748664,5.831834235,5.216240745,6.051293534 +ATP5PO,9.15762633,8.102368085,7.537794936,6.520404204,9.712771533,9.209934263,9.012962132,9.607568307,4.790650271,6.122202109,6.665898602,7.027008668,5.043026803,6.044557307,4.449190509,8.216086856,8.421400145,8.511347193,7.889067292,7.457494246,8.487491938,8.173237634,7.65300603,9.174967608,9.224591724,9.702335677,8.012239045,9.373690038,7.835535889,7.884015689 +ATP6AP1,6.028175184,6.64740085,5.814343738,6.123818126,5.913505043,6.501636817,6.199508691,6.267467098,5.524773096,5.711461302,6.185680717,6.043136018,5.001440828,5.182723848,5.782446597,6.487966448,5.57310093,6.678744188,6.109117488,6.588274612,5.628898763,6.934919098,5.587586866,5.94213915,5.469109381,6.079051935,4.321245613,5.938667766,5.547728792,5.798872914 +ATP6AP1L,4.53005292,4.348352881,4.801648339,4.092905002,5.117284972,4.464749899,4.485398557,4.370693641,4.902833102,4.116725794,4.657284449,4.432293734,4.747401245,3.949989055,5.348316671,4.254826022,5.034227178,3.470834705,4.600420544,4.339154654,4.099706263,3.351750988,4.937429746,4.678991499,4.948261557,4.379332428,4.332283487,4.656489613,5.306240537,3.378753509 +ATP6AP2,5.674445911,5.567615982,5.516710787,4.749295395,6.24151847,6.032212462,5.915040272,6.47120789,4.507378087,3.926372788,5.012068813,4.033026447,5.34946217,3.55801046,3.636933802,5.475716149,4.94458246,5.124018901,5.706241452,5.546528842,6.004935063,5.712471198,3.758528578,5.75805355,6.193650378,5.98658745,5.732865206,5.744479961,5.089693968,5.284706631 +ATP6V0A1,4.766955302,5.075662737,4.236849716,3.059635473,5.509547314,5.441482224,5.605135189,5.300445175,3.460106426,4.391269478,4.874412111,4.499294622,3.738497156,3.694502348,4.103098252,5.133282393,4.591276692,5.470700034,4.889615211,5.026863271,5.03845991,5.039836528,3.256720763,5.481647474,5.183841022,5.312038859,5.140551421,5.189481271,4.650120353,4.031913294 +ATP6V0A2,4.919220962,4.069214741,4.397490965,3.607484376,5.123509134,5.178722659,5.229137805,5.471290671,3.194052201,4.391910687,4.198027819,4.931279268,4.134856852,3.996897073,4.689295429,4.716615727,4.326985929,5.140412508,4.758474672,5.7193628,5.363636341,4.946884852,3.147676183,4.978134104,5.135895444,5.07911628,4.978667573,5.122922494,4.098521105,4.503213669 +ATP6V0A4,4.969226945,3.611178132,3.584817483,3.784412751,5.181815669,4.447654678,4.736686546,6.394397341,5.011780534,4.959788892,4.764764863,3.885219069,4.829799956,4.702551116,4.939601689,5.352822926,5.539961258,3.221806619,6.071667317,3.905991217,4.487399554,4.924124957,5.449504338,5.857269758,4.618361209,6.624470978,4.128858373,5.486522756,4.715367393,5.41691988 +ATP6V0B,5.440709436,5.118082309,4.576401433,3.548657812,4.683800794,4.902878924,4.091693882,4.406626006,4.619700171,5.092892104,4.079254548,4.608821166,4.650139023,3.621414613,4.694868656,3.747057692,5.435063909,4.344829883,4.513599558,4.45222745,4.676615234,4.933073294,5.320446906,5.436164451,4.729189413,4.889275917,3.916545851,4.569746019,4.854374693,4.477787885 +ATP6V0C,5.838010675,6.060047734,5.200835232,7.009704377,5.55942885,5.953519828,5.670179558,5.985197415,5.896545717,6.342725198,6.875807792,5.633400631,6.09701025,5.693710058,6.467797877,6.040656187,5.125582536,5.586807633,6.333967992,6.007120944,5.26498883,6.097071601,4.836412655,5.902162195,5.750312083,5.846525687,6.425135661,6.329703867,5.189479792,6.775352399 +ATP6V0CP3,6.065537406,5.954680261,5.639988821,6.514909249,6.4278825,5.969430455,6.447860501,5.986062449,7.357844609,7.692825516,7.076644606,6.2039926,5.92061125,6.289103989,7.755058892,6.522410546,6.248681587,6.289350392,6.4278825,6.583963898,6.51230234,6.219528905,6.427389528,6.203560621,6.429851728,5.836559344,6.764552351,6.357945438,7.175404006,7.901097743 +ATP6V0D1,6.170194082,5.59341848,5.229001044,4.521300353,6.210495863,5.463151523,5.780274975,6.05458821,4.570218789,4.484250876,5.084573368,4.206499653,4.507300537,4.2102741,4.335079596,5.889173108,5.598663641,6.969391981,5.100878657,4.9525162,5.272771733,6.088975686,4.846053469,5.795180734,5.432257289,6.218809574,5.161583803,5.708807281,4.454490337,4.348503176 +ATP6V0D2,2.737875926,4.556963449,3.314827709,3.229342425,2.934869728,2.619808665,3.851715594,3.400694345,2.582969068,3.082951094,3.415025534,3.071549315,3.152884399,3.962357533,2.580045351,2.634848547,4.096186857,2.543853629,2.375950349,3.18142309,3.776732732,2.184179529,3.510242507,2.594242407,2.251308423,2.976790872,2.898228631,3.267687697,3.692978134,2.856438929 +ATP6V0E1,7.573627282,7.675726179,5.886324956,6.889370028,7.438497815,7.743848874,7.56564005,7.342220821,5.486368903,6.027216424,6.636578419,5.969327611,4.948007904,5.907907597,5.824973821,7.069398218,7.649570257,7.729308175,6.891720941,7.112105013,7.224979278,7.301175412,5.752110729,7.292929333,7.249145396,7.63940357,6.655338175,7.119971338,5.16247736,6.434419223 +ATP6V0E2,4.889501648,4.905931008,4.713896493,4.778066221,4.068595668,4.219931152,4.330085545,3.572407717,4.615988942,5.475172743,4.347396784,4.163659809,4.067745894,4.708357233,4.426107374,4.177144281,4.025619793,3.318901847,3.708202632,4.814519067,3.70784381,4.102015147,3.584710227,3.624542955,4.26587196,3.462797831,3.903231482,3.632904082,3.264127127,4.819373943 +ATP6V1A,5.914095903,6.029120159,5.088001768,5.118726519,6.08419774,5.890333727,6.148447895,5.541407019,3.988902943,4.533300578,5.681439213,5.521194529,3.948047855,4.132024229,4.908329931,6.326366551,5.467147712,5.741407229,5.705173002,5.3275894,5.739500268,5.953210636,4.776700389,5.812173297,5.5716169,5.560199913,5.188412355,5.929142992,5.055577121,5.367477634 +ATP6V1B1,4.945183473,4.617060416,5.520282811,5.001122861,4.981137507,3.977991235,4.374126187,5.490950905,4.442037994,4.957732925,5.084667926,4.652573477,5.194411537,5.544351438,3.971651981,4.523259707,3.903284098,3.9949274,4.9206989,3.60871783,4.403382801,4.881199829,4.604291738,3.593115743,4.54399152,4.523259707,5.039497036,4.926527268,4.251138352,4.696533778 +ATP6V1B2,6.788722137,6.376748484,6.052921574,6.035186934,5.742777875,6.673385423,6.226101855,6.82589043,4.781037518,4.594124942,5.365769133,4.4739853,3.201210829,5.951027187,5.547922768,6.467894423,6.234561185,6.601564413,6.41847907,6.78886254,6.28512986,6.762812786,5.331721226,5.979371469,5.860719906,6.395731024,5.513698789,6.148176706,5.631880282,4.977208425 +ATP6V1C1,6.20842463,6.290573569,5.066669587,5.019925383,6.251113273,6.20790215,6.106141106,6.738099105,4.206635127,4.87641819,4.432700389,4.672842502,5.212661385,5.00173569,4.34127056,6.222602045,6.412570634,6.605351849,5.876301911,5.383412429,5.409233321,5.937518343,4.727106623,6.031934219,6.415231198,6.514009276,5.311500465,6.254690217,5.61214047,4.653203341 +ATP6V1C2,6.05529854,6.830413728,6.290606074,7.120970446,6.908085974,6.499851928,6.634167448,6.690502477,4.715793125,6.395728384,7.98189126,6.046310821,4.936604587,4.857441766,5.872214996,7.158173694,6.688416517,6.683732133,6.465162823,6.641982134,5.646585784,6.961744332,5.652344376,6.787607364,6.420098656,7.413380823,6.550595759,6.465596221,5.275917669,5.189686592 +ATP6V1D,6.745738832,6.899534654,6.182477361,5.318244208,6.630118235,6.391869386,6.657590941,6.503061404,4.87383983,4.169250339,5.27571338,4.782235318,5.3616042,5.520813287,3.116185638,6.517291297,6.54179861,7.102934755,6.997311202,5.527519199,6.273067199,6.691232204,5.637729714,6.632765428,6.42863554,6.718498776,5.614918841,6.797364652,5.812847079,6.160335112 +ATP6V1E1,8.330329097,8.291854684,6.773958597,6.752625845,8.957681545,8.65202877,8.017719854,9.057863329,6.915632663,5.538491027,6.013998351,6.429788762,7.449710493,5.408234376,5.841442083,7.836574356,7.222849934,7.932998677,7.367822079,7.400038412,7.080792521,7.534272776,5.693028968,8.510625332,7.917169043,8.267039731,6.583360458,8.161651073,6.770623102,6.801208277 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+CHID1,5.645346903,6.095777731,5.715409621,5.695997206,5.673212922,5.703728459,5.986799766,5.468046529,5.451723327,4.344427323,5.313394372,5.512689395,4.830816744,5.940776205,4.42867601,5.927283115,5.426215455,5.865564472,5.809518744,5.896177037,5.401676229,5.77048361,5.039091625,5.627288769,5.749848853,5.667566203,5.887814034,6.084342709,4.5016851,4.901476012 +CHIT1,2.857057813,2.596984546,4.034142469,4.273501897,2.603524784,3.683316932,2.607628037,2.565946335,3.708018218,4.484313519,3.286653544,3.021621685,2.775156379,2.560433699,1.979528262,3.299496911,2.69532972,2.594889748,2.549686719,3.002518567,3.962745656,3.390728282,2.248447365,2.301973154,2.414611257,2.675157104,2.838390647,2.631039971,3.376665931,3.91851682 +CHKA,3.720372629,4.805461832,4.358693365,4.626468572,4.049547668,4.896234872,4.694165546,5.017221725,3.761199149,3.484950669,3.083490973,3.214241611,3.864508464,3.820181307,3.844667747,4.409675322,3.632249284,4.160400652,4.294342921,3.25738121,3.77048118,3.896075734,2.740434895,3.407222213,3.500519299,4.292329667,3.644001318,2.783542113,2.709073941,3.474702169 +CHKB-DT,4.441101242,4.354527391,4.128558353,4.465799724,4.556084388,3.841844319,4.048009512,4.09938862,4.48153982,4.688936654,4.685394406,4.981630995,4.74931111,4.805001301,4.088393281,3.727340392,4.787328356,4.40559017,4.30077862,4.1919282,4.049440868,3.527824326,2.959075641,4.086317311,3.535495807,5.006538622,4.406738078,4.292417156,5.393121367,4.294352964 +CHL1,5.1422826,4.56976373,5.79323393,4.71657566,4.674959837,5.547344033,5.264281405,6.191479975,3.972265678,5.197193066,5.517623286,4.679882512,5.204968921,4.574182008,5.093740663,5.140804544,5.515211729,5.145266744,4.697321011,6.166287221,4.734530044,4.973247875,4.629943715,5.831229801,5.455998047,4.67167011,5.075270507,4.426261029,5.027831514,5.216407031 +CHL1-AS1,4.486196793,5.720169875,5.470538971,4.582184292,4.335225225,4.869257492,4.930337431,5.598024985,4.396019594,4.218146146,5.43505173,4.456534736,4.901906737,5.135122838,5.252993624,4.69635862,3.512168942,4.724779592,5.778815778,6.086115664,4.003245595,4.824103641,4.515361715,5.334161049,4.114848494,4.121305742,5.202607075,4.473052514,4.744454502,5.614491535 +CHL1-AS2,3.753160101,3.38068235,4.758927583,3.945044309,3.29260354,3.979781474,4.151987583,4.024654811,4.303286904,4.989880277,3.830220619,4.732466319,3.932700979,4.054058665,3.830220619,3.304762826,2.476228692,3.291147631,4.113275158,3.755383978,4.261127659,3.131288905,4.411053444,3.94060289,2.646106948,3.50402697,4.411545526,2.579797606,2.996240877,4.049021613 +CHLSN,3.324586293,3.205993539,3.147364951,4.174066364,2.729907636,3.442411104,3.555987343,3.120477232,3.959410598,3.887228578,3.545707084,3.176994776,3.414243477,3.541398578,3.694734427,2.743518177,3.064126306,3.867565727,3.052988481,3.73514131,3.388885182,3.200791641,4.335268647,2.554524899,3.092362891,3.355127412,3.547404914,2.852817212,3.442411104,3.182201187 +CHM,4.60046047,4.41494703,4.191292349,4.555896336,4.914338881,4.877713451,4.466627263,4.806701966,4.410719306,3.982393371,4.477654435,4.387060481,3.66152417,3.882048929,4.10659094,4.419259079,4.403563404,4.235573166,4.729509493,4.514650039,4.741126479,4.29636639,3.733890258,4.930000124,4.622304693,4.848794566,4.085034661,4.783960083,4.047933942,4.690545991 +CHML,4.560967476,3.98325229,4.064358121,3.335967539,4.941724498,4.374988881,5.110332115,4.596572122,4.956218491,4.742955318,4.280159435,5.006511398,4.080664485,3.897667556,3.831072758,4.520600771,4.290475497,4.468590266,4.272714928,4.443512816,4.186908931,4.614388645,3.692233504,4.53058507,4.563365028,4.944702826,4.337194765,5.068574865,3.41433502,5.01577296 +CHMP1A,5.665823998,5.687365041,5.960624569,7.008623401,5.700727465,5.2451193,5.675489298,4.697222961,5.344996541,4.591195549,5.725334801,5.440953578,5.398666004,5.53129322,4.925390766,5.703226419,5.13672507,5.679669587,5.594168936,5.981950572,6.24208228,6.498180215,6.166809229,5.499900029,4.879305156,5.978479651,4.298026786,5.687453855,5.217198819,5.791362952 +CHMP1B,7.654988287,8.170180232,6.548273373,6.808013634,6.826081424,6.764117466,6.942739219,7.07618094,5.599167682,5.153818269,5.329459139,5.990551827,5.314799384,4.579965615,6.017534342,7.361727051,7.399399901,7.485875103,6.984208682,6.71812048,7.062287083,7.390383191,5.809544261,6.857336045,6.47109668,6.825009328,6.784726794,6.926996821,5.39716925,6.088978369 +CHMP1B2P,2.676854524,3.490388365,2.975106271,2.828884567,2.745160536,2.882264001,3.075428681,3.238136556,3.406997228,3.505698545,3.387176201,3.038125648,2.319131283,3.370738755,3.184098718,2.944363728,2.837407942,2.694869007,3.014880114,2.739971646,2.802780673,2.727634476,3.483923031,2.922560864,3.032076653,3.102166335,2.795624922,3.797686525,3.059877597,3.011779428 +CHMP2A,6.472414014,6.143612396,5.727181752,5.812463056,6.81996319,6.38495446,6.457695893,7.000118126,4.448645387,4.426260688,4.958858168,6.036765232,5.913704942,5.628269473,5.647765121,5.928537702,6.190206308,6.170590238,5.724052331,5.986861909,6.108192054,6.171930597,5.510014063,6.370068831,6.670473495,6.796543781,5.418809375,5.953156072,5.661229373,5.352788262 +CHMP2B,5.80224028,5.627108021,5.111154352,4.884696849,6.321829999,5.993792811,5.907290784,6.336232174,4.38090215,4.216267228,5.015553553,4.658369533,4.713651668,4.67824907,4.623774697,5.247513743,5.999096359,5.643041029,5.395098338,5.399489065,5.226697985,6.062491617,4.892728428,5.976464395,6.028575976,6.305696735,5.044423477,5.155897331,4.767194781,4.86062761 +CHMP4A,5.793211753,6.06304744,5.921773081,5.758572041,5.794526995,6.443276191,5.828451087,6.602030652,4.107635755,5.142696925,5.498728532,4.709314864,5.22894236,5.148348493,5.256319306,6.150515841,5.818048667,6.180819781,6.148453316,6.3178686,6.241910112,6.157359033,4.901507442,6.164101829,5.683742601,6.359250518,6.336072329,5.458709169,5.017670378,6.119037676 +CHMP4B,7.748409445,7.669545036,7.878026211,6.719162049,8.005388704,7.851779992,7.576408,7.833623712,5.848970784,6.472774876,5.894212189,7.505404395,4.981277579,5.200116622,6.696582332,7.5618969,7.258127845,7.96412637,7.757350835,7.923964378,7.23915813,7.893053975,6.273603598,7.311305803,7.613002453,8.036406465,6.955965309,7.188122626,5.480529745,6.900896062 +CHMP4C,5.527227048,6.059419319,6.586608104,6.160463911,7.023047959,6.745347605,7.196863418,6.616669154,3.412306241,3.99263677,4.998367092,4.41664204,3.918892387,5.018538982,4.678111076,6.151408241,5.595135394,6.254096983,6.465696595,6.737535478,5.226475779,6.809695964,5.795533643,7.219505416,6.663345758,6.398644631,5.57435628,6.037118029,4.35179342,4.541528162 +CHMP5,7.212327349,7.400044942,7.199277604,5.275402759,7.054516011,7.072164119,6.998414803,7.568318419,5.149941846,4.874412111,6.108211707,6.450127679,4.481497648,5.36894656,5.295568785,6.754758486,7.38373102,7.684129289,7.539656777,7.196110525,7.421660048,6.721146172,4.524588375,6.864456399,6.782004819,6.586273949,5.912269992,6.070088794,6.586273949,5.125682867 +CHMP6,4.972747874,4.913594952,5.191486716,4.719583025,4.858288161,4.51373658,5.000393512,4.337645542,4.60224356,4.966324203,5.491615177,5.120383468,5.299552445,5.082211791,4.867831797,4.549608165,4.575781236,4.980919337,4.267140478,4.808949566,4.651989936,4.832246072,4.47554002,4.288612402,4.040319925,4.379179051,4.735249273,4.317570011,4.627676171,5.065484202 +CHMP7,4.47522793,4.243701846,4.795872106,4.456408899,4.830968308,4.781261385,4.312159706,4.434156585,4.485659647,3.966839244,4.051453292,4.76064906,4.799348004,4.273314448,4.633602424,4.649795378,4.631262451,4.686628413,4.22451632,3.961747694,4.418956061,4.750721399,4.863756634,4.21721036,4.142421941,4.378135196,4.116035462,4.108586597,4.014220558,3.382524863 +CHN1,4.959291467,5.391416315,4.328341,4.345405614,4.308434408,5.870337404,4.984528186,4.755387867,4.185135401,4.926456762,4.073388616,4.866986935,4.76406949,3.453935368,5.596041837,6.007263799,4.786994829,5.599864356,5.416160628,4.899492769,5.256679928,5.36806512,6.373557363,4.530178643,4.705378898,4.773612727,4.664192338,4.809590132,4.832859926,5.31068307 +CHN2,5.906160761,4.748298906,5.242944341,6.174534443,5.931779144,5.694599226,5.092913075,5.468850359,4.904833012,5.635647089,5.139227743,5.93197768,6.104105085,6.05926543,6.164737953,5.014183093,5.78658681,5.595250133,5.576191508,5.64257104,5.38008618,5.738539879,4.993952744,4.882138269,6.024716822,5.272054988,5.942595518,4.734247152,5.326212928,5.194080912 +CHODL,3.76550835,3.843829635,3.431131935,3.957279247,3.672964527,3.48097328,4.027299211,3.293765311,4.509829422,4.29193,3.762962353,3.839442643,4.125820437,4.143609094,3.715397795,3.098948552,4.049146361,3.680303269,3.991526634,3.202203042,3.891386368,3.28305831,3.864449922,3.865167738,3.472906829,3.209428595,3.323549995,3.406478595,4.1177239,3.984309344 +CHODL-AS1,4.147151164,3.597150732,3.186101324,5.055195055,3.424758662,3.738562663,3.510378726,4.146138848,4.672431598,6.019122442,4.862113977,4.186808318,4.393002332,4.868729924,3.7434338,3.944315792,3.901010947,3.631686174,3.554283124,3.535183787,3.776274042,4.177948948,4.140876715,3.595601219,4.214451255,4.06372122,4.632461744,5.268103476,4.760660193,3.592712182 +CHORDC1,4.043351365,3.825888538,4.592078586,4.874149936,4.172833923,3.161186905,4.599979512,4.258706437,3.362947583,4.507409261,4.232458019,3.774658012,5.168949665,4.334553139,4.736862266,3.983679182,3.361080624,4.800589449,4.112873827,4.580348404,4.684692284,3.94660112,4.284906737,4.598689959,4.157252951,3.941294581,4.035452855,3.851153775,4.819389544,4.443920927 +CHP1,6.524563181,6.490234128,5.192537392,4.96810673,6.395837522,6.154703317,5.695874141,6.154703317,6.246993794,3.799604835,5.312934594,5.646531864,5.080127678,6.060622454,5.441130681,6.394871317,6.390709061,6.702559003,5.622812447,6.420366591,5.713610268,7.030008894,3.923707902,6.212887933,5.952423348,7.856073628,5.484936948,7.506629756,6.575630189,5.072542327 +CHP2,4.812944183,4.027662021,4.715715943,5.16287332,5.048369196,4.956040576,5.473410619,5.474677354,4.117299191,5.614788704,4.270750263,5.28646802,5.595043079,5.608126508,4.670192852,5.387389857,4.247777696,4.342412168,4.381236511,5.305999694,5.272088631,5.052116182,5.176478737,5.680455169,5.08816082,4.703084821,4.168847597,4.929277114,4.665328588,5.319492393 +CHPF,4.176694311,4.889621287,4.933057113,4.971886874,4.691837082,4.042905706,4.546706091,4.342549626,4.336746422,4.525811864,4.091371065,4.300749042,4.094651185,4.186712351,4.305530537,4.271883826,4.151462967,4.748833759,4.653351919,4.827903391,4.313690689,4.397148218,4.559845591,4.161599607,4.311277714,4.532833189,4.903271948,4.014944809,3.540184715,4.791468432 +CHPF2,5.597570298,6.240474737,5.286818519,5.393310339,5.230840471,5.060700365,5.514695451,5.443028069,5.774793372,4.780715025,6.008184459,5.429263639,5.033560531,5.461842292,5.765101587,5.680623768,5.59314588,5.485364377,5.76606519,5.526557608,5.800100538,5.303362069,5.849710749,5.171309919,5.122216387,4.783726108,5.116659825,4.946054571,5.641435017,6.363497066 +CHPT1,4.995345804,5.380252098,5.023713755,4.656792644,5.463621728,5.159339985,4.974364006,6.133582456,4.296508993,5.009119982,4.885306412,4.69594849,5.345326519,5.277113619,5.264586881,5.294533204,5.411730788,5.081717516,4.986219788,4.832065156,4.920914332,4.909418194,4.594094328,5.540590036,6.169355791,5.484327072,5.584672909,5.815966377,5.21829952,5.424708861 +CHRAC1,5.300118407,5.691945677,6.171498107,4.552811226,5.73585106,5.657589174,5.636138019,5.462831959,5.219049521,4.077814736,5.892580063,5.829122647,4.599657267,4.426845953,5.213764505,5.636797779,5.462205113,5.106106368,5.061029835,5.15001299,4.976469852,5.300118407,4.53201458,5.219642095,5.513355128,5.701277818,5.304632825,5.480549891,5.770335845,5.695958218 +CHRD,4.297951908,4.352072358,3.904368301,4.612213838,4.450241396,4.027351022,4.633016115,4.267157112,4.49974936,5.471968256,4.596280651,5.136988188,4.108087794,3.863389754,4.046487086,4.054445905,4.928414663,4.928822973,4.338503008,3.479308165,4.261135766,4.307929014,3.981661433,4.217212465,3.795129117,3.582225157,4.046502946,3.339940724,4.880415692,3.591110955 +CHRDL1,4.934474943,5.933382325,4.76181485,4.275228564,7.129869264,7.43938231,6.766088169,7.174467009,4.869967321,4.701572047,3.599777381,5.088165015,3.951237858,5.645809943,5.27553475,6.484291195,5.003291901,5.716525415,5.465004417,5.504699716,5.693801306,5.57462915,6.212448514,6.199966624,6.572813621,7.028827762,4.605048164,5.904147339,5.253925713,4.546859281 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+CLIP2,4.236862091,4.93579084,4.797843063,4.232044451,4.440004138,4.771228673,4.576074594,4.764722706,5.147029425,4.98399976,5.461584844,5.199799466,4.764722706,4.530036878,5.606546982,4.461507012,4.531719875,4.999686782,4.829733696,3.948508411,5.042540494,4.564416256,5.152613998,4.800574112,3.728492091,4.891078234,4.856488224,3.919406817,4.255607772,5.081867568 +CLIP3,4.677869511,5.128845363,4.670292428,4.815760736,4.80204751,5.116515168,4.757425566,4.713910705,5.014157906,4.844899623,4.074248946,4.374701858,4.87183427,4.937508742,5.315315546,4.767398558,4.680712456,4.454805478,5.023080841,4.236048936,4.03767504,4.559740298,4.859296626,4.340390322,4.793447686,4.693992331,4.560449041,4.517414164,4.677869511,4.080653277 +CLIP4,6.829384944,7.193113496,6.433628166,6.407688253,6.960491614,6.325924197,6.806921812,6.889103935,5.955007711,6.816651774,6.319963634,6.612043456,5.619359362,6.762938935,6.249929939,6.770269047,6.383988412,6.610838198,6.625505267,6.625505267,6.441815003,7.085789167,6.186305499,7.172570116,7.054463361,7.421315438,6.322932928,5.965763905,6.195215761,6.417670735 +CLK1,6.931394097,7.075161867,6.275905757,7.056503866,7.645042202,7.624364066,7.581606587,7.819361164,6.298163071,6.415903095,6.630769139,6.693436625,6.268735118,6.739999768,5.66412842,7.630660814,7.377694799,7.225852592,7.324960108,6.854581477,6.943007942,7.056503866,6.27626714,7.792687078,7.519735014,7.150975204,6.927548394,7.699925157,6.815876864,5.986129066 +CLK2,8.395042753,9.075494071,8.947966806,9.265528898,7.18595372,8.30519453,8.259865484,7.201066228,8.107294596,8.275364501,8.510803734,8.879726906,9.100993973,8.415426599,9.27164315,7.922613988,8.170996746,8.213279775,8.487490132,8.511139406,8.721317594,8.770137491,9.329007019,8.292589907,7.697823088,7.458903484,8.315237774,8.231773227,7.41737657,8.828208286 +CLK2P1,2.936035104,3.08154114,3.763196696,5.223540057,4.335479534,4.975580728,4.193853421,3.175004571,4.113004958,4.4256209,3.889859827,3.829630878,4.864227798,3.862353723,4.419579704,3.404290462,4.572004718,3.536572212,2.636326745,3.718705716,2.623874299,4.291227226,4.389008885,3.614159554,3.829472012,3.671326806,4.05366771,2.270595853,2.732898255,3.994079483 +CLK3,5.594450377,6.023149125,5.287098041,5.235163265,5.113677487,5.138649802,5.972847858,5.024772118,5.48932563,5.058675066,5.557641167,5.821916629,5.581173541,4.946553574,4.811749759,4.52210618,4.536557488,4.950954281,5.193531759,4.868744788,4.898471921,5.151354376,3.262638319,4.736482808,5.111721001,5.021694262,4.791445001,5.246432772,4.387227263,4.674589741 +CLK4,6.111470544,6.711360607,6.994649139,6.491091799,7.444307733,6.952162363,7.512611984,7.092214453,5.488118222,6.823271624,5.738979955,6.458842922,6.271543879,6.428762915,6.058590049,6.714206324,6.749745521,6.529065602,6.965225517,6.396232062,6.683713402,6.924827437,6.460051864,7.360222644,6.999956839,7.010643608,6.78481768,7.286105554,6.696678506,6.308822263 +CLLU1,3.643755034,3.657809364,2.601292927,3.810393556,3.690177662,3.318069622,2.67693671,3.190492241,3.144583361,4.160359553,2.749913912,3.621607051,3.350614364,2.992278623,3.672125484,2.974416029,3.801452738,3.223841139,2.707530447,2.589312819,4.35066403,2.87283839,4.034315605,3.815584295,2.730110196,3.292142835,2.733013058,2.515778859,4.414199925,3.718885249 +CLLU1-AS1,4.340409057,4.698817103,4.882070031,4.528598667,4.031860947,4.162747904,4.584468618,4.333402196,4.796022033,5.620609273,4.328266591,4.744959744,5.162598393,4.131685191,4.910618896,3.962211684,4.472134323,4.150066444,4.090242199,4.470885806,4.356172001,4.524373517,4.977837666,4.501916837,4.969264303,4.270402239,5.232525454,5.014628114,4.748603328,4.272993613 +CLMAT3,3.4202401,3.739776641,4.490690132,4.730420513,4.201387201,4.5162375,5.052994796,4.089252512,5.275638191,4.810390008,4.010957876,4.756070489,4.893423001,3.811756062,4.334107414,4.808839788,4.602173262,3.522707597,4.899883669,4.774779066,4.277842392,4.517512151,4.829776495,4.324879294,3.814387777,3.533183308,4.248167217,2.664918222,3.555837795,3.953017756 +CLMN,4.321175139,4.474712761,4.718072194,4.166616663,4.569630999,6.301373795,5.456884883,6.346451944,3.815874152,4.454032782,3.782714732,4.679100556,5.484540829,4.664343707,4.424692577,4.892233446,4.679100556,4.209332485,4.412327947,4.985936638,4.117163033,5.838860025,5.183731751,3.785810899,4.773900596,4.5288461,5.122703568,5.063995722,4.237746734,4.908805325 +CLMP,5.954423234,6.818603819,5.373021255,5.233717131,5.97277651,6.668726616,5.687572747,6.024179725,5.494521226,5.101567772,5.742460433,5.397617605,4.236209161,6.18089422,5.816335593,5.369661354,6.459906746,6.092341578,6.347410646,6.103201388,5.994692791,6.289827221,4.699493129,5.476317838,5.644754509,6.576646917,5.28728694,7.554249616,5.29685463,5.924534213 +CLN3,4.647984491,5.374597876,4.983361664,4.134475141,5.537925963,5.531112442,6.436805857,5.374598336,5.420502531,4.735694408,5.034495395,4.504861198,4.530445288,3.559737519,4.419153835,5.471197615,4.177380398,5.796850468,5.259196864,5.006858111,4.488516689,4.853657021,4.860647271,4.936630662,4.891305458,5.135077869,5.217198819,3.874184737,4.882004142,4.752594122 +CLN5,5.683109288,5.306418522,4.775799907,4.740847193,5.519190304,5.85204324,5.45223119,6.029280572,5.316418721,4.032287234,4.893064983,5.221602425,5.458306609,4.857098879,4.757822031,5.240114875,4.235887152,4.672051455,4.883772838,5.103067617,5.489174205,5.327462935,4.761276896,5.914337919,6.046814718,5.91609866,4.733351074,5.168699702,4.119434868,4.133394814 +CLN6,4.165171127,4.813840985,4.062547963,4.411304968,4.888516032,5.046205443,4.959961044,4.352887529,4.213468289,3.169327361,4.292953004,4.213468289,3.711359988,3.732735426,3.323428702,3.270498654,4.056252446,5.332525464,3.572870842,4.219897651,4.219003051,4.055650602,4.167218059,4.977763634,5.11611423,4.585924118,4.300601513,3.437668253,4.683070444,4.14324675 +CLN8,4.210132773,5.376228267,5.125024017,5.41964562,3.765194456,4.542580807,4.410637388,4.571689485,4.431780691,4.8502237,5.220150884,5.22695712,4.004849449,4.4748813,3.97866408,4.398315975,4.654401648,4.854437543,4.577132256,4.565267981,4.836068648,4.547084199,4.191498566,4.38115605,4.522093855,4.822828947,3.915696339,4.525239534,3.940834739,3.835746777 +CLNK,4.195296877,4.323540305,4.89174977,4.604099142,4.165026645,4.339065083,4.30693657,3.808638037,4.124925563,4.343234191,4.022563099,4.195660876,4.325161421,3.630820643,4.119399665,3.823184178,4.212441138,4.15535937,3.945498522,4.306145389,4.22365207,3.908043638,4.210447396,4.03015702,4.168579908,4.422493546,4.186503709,4.185927252,3.710865957,4.114103534 +CLNS1A,6.546005503,6.459645856,5.217307261,4.683806349,6.449105976,6.620453556,6.576613421,6.768862194,5.178443365,6.507751781,4.820793478,5.957831304,3.829795461,5.459319216,5.521516393,6.398807953,6.333922459,6.345453327,6.51699697,6.427854954,6.521740947,6.265699745,4.310339038,6.973126272,6.925853737,6.828958427,5.66969937,5.873919624,5.901800935,6.153425124 +CLOCK,6.09366069,5.950263249,6.410569487,4.864275409,6.766225778,6.330638352,6.622402496,6.291889516,5.432624409,5.189345894,5.572283332,6.551432345,5.752095062,5.475763892,5.678672864,6.518770076,6.336197816,6.286755702,6.007961997,5.876105765,6.017878835,6.3322752,5.589474559,6.23081463,6.365666402,6.743234703,6.422865208,6.09366069,5.347337908,5.737313958 +CLP1,5.350707872,4.969605554,4.373903942,4.902594143,4.877039817,4.429789176,4.726713924,4.120382172,3.532223788,5.379136582,3.733982442,4.739708574,4.088121992,3.75365273,5.058641422,4.324879294,3.813590437,4.669413026,4.974582909,4.485884987,4.049881685,4.041908071,4.548200464,4.548200464,4.634363646,4.816063069,4.128858373,5.107248248,4.538923141,4.220531253 +CLPB,5.100577364,4.675865724,5.727637994,4.226021659,4.864175719,4.334922429,4.538939961,4.943301873,4.448032036,5.227535934,5.219896569,4.953240041,4.697348226,4.565331365,4.810565715,4.908028228,4.391870026,4.834619914,4.305565215,5.304463236,4.393274174,4.739746126,4.936341942,4.303178974,4.615128726,4.442665994,3.699478129,4.294748828,4.323783868,5.162531169 +CLPP,5.55301475,5.547220773,5.940929502,5.270066648,5.744692292,5.811295309,6.073683693,5.624979295,3.897082741,5.086739758,4.972166808,5.041547229,5.720393355,5.079855745,5.898453004,5.668675025,5.763060873,5.760322253,5.539844437,5.381157167,5.456739472,6.007018572,4.9213682,5.456668727,5.542239338,5.422939319,5.363088519,5.771041767,5.387936381,5.688518585 +CLPS,3.179456758,3.027111379,3.946416339,3.274344705,3.396115923,3.30689515,3.317833516,3.518956654,3.99672632,3.54721898,5.410327231,4.460032653,3.871245007,4.249592656,4.328730781,3.084671046,3.52588503,3.471622443,4.384692595,3.9735854,4.454776133,3.899660589,4.712457083,4.091824739,3.904155566,3.356653787,3.481237317,4.394794842,4.108644803,4.480088537 +CLPSL1,3.000275989,3.273937222,3.14059384,3.68982521,2.424904763,3.0703597,3.541811642,2.586691128,3.7268911,4.212120431,3.815799269,3.301164992,4.813131006,3.394248491,3.504382072,3.447685864,3.274350095,2.656274106,2.965972252,2.947921344,2.801791841,3.249925521,4.705067906,2.908168641,2.826000927,3.438557663,2.794973273,3.095956201,4.197860236,3.781005371 +CLPSL2,3.939356023,3.727853447,3.727853447,4.444082526,2.867329494,2.735493847,3.313935462,2.601170506,4.823135856,3.909664631,3.484415325,3.740491082,3.964664045,3.90049055,3.332334063,4.430002024,3.384586901,3.93719117,3.669187822,4.541427417,3.149991825,3.683803557,4.626679747,3.727853447,2.894388413,3.78546857,4.073539697,3.609481507,3.727853447,3.665935472 +CLPTM1,5.887891184,5.76265928,5.615027944,5.794793112,6.320129839,6.289909679,5.965273829,6.076116749,4.72919088,4.950032963,5.996163372,5.360248029,5.04891063,5.383643117,5.69024845,5.866086603,5.839904251,6.135203737,5.742151506,5.664847247,5.639334689,5.941072858,5.38697837,5.752019534,6.449571143,5.842717054,5.050883085,5.689267072,5.263609457,4.65701496 +CLPTM1L,6.342210591,6.373165472,5.963858572,5.665622215,6.239064612,6.366783155,6.157718972,6.213941288,4.348933783,3.841006193,6.223809624,6.020170104,4.761756416,5.018849034,5.132164311,6.475835612,6.186601115,6.692233038,6.251063705,6.066663976,6.566734366,6.991422354,5.916517883,6.212887933,6.007771613,6.114853024,6.019210062,5.938938033,5.300118407,5.89893796 +CLPX,6.967069798,6.537117421,5.755253099,6.037968301,6.825238466,6.742742733,6.780755899,6.858169704,5.854865197,4.932846081,6.359455556,5.620660623,4.084240054,5.071959847,6.321323728,6.598670749,6.410733319,6.974385403,6.520794692,7.111936096,6.520794692,6.691685337,6.044862644,7.225732911,6.893153319,6.964886175,6.53298125,6.593730333,6.08661638,5.8030497 +CLRN1,2.97049204,3.182364832,3.311103664,4.439867041,2.463132415,2.900459396,3.283003905,2.678541498,4.364754083,3.853132727,3.356753699,4.655550429,3.387756666,3.093921576,4.254306548,3.273909396,3.926763342,2.554692471,2.668945873,4.009898515,2.947317593,3.054154799,4.267371624,3.189875477,3.223242117,2.892158582,2.913922306,3.219021594,3.099447176,3.71793169 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+DCK,6.280481709,6.376830971,5.860848445,5.999266553,6.045896338,6.29559447,5.874724925,6.273699827,4.004831206,5.501829656,5.279622491,5.464479319,5.583359722,5.303298417,5.500462099,5.884322123,6.34286374,6.695238164,6.994187723,6.313155518,6.992963607,5.623769258,4.907277333,6.255610617,6.357601271,6.131111497,4.772554341,6.212887933,4.991092271,6.022282105 +DCLK1,5.730619419,5.781922629,4.440337804,4.059685956,6.314258355,6.885875736,5.640938877,6.682095981,4.854126528,4.092993551,4.164725722,5.333482965,4.792883524,5.409635419,4.766959417,6.316927795,4.730095978,4.423312727,4.682683814,5.536943956,4.874387079,5.404511897,4.727404909,5.869112657,6.282581339,5.341068567,5.097689529,5.459081267,5.3523339,4.691328455 +DCLK2,3.432005695,3.576538021,3.060644232,3.672964527,3.525396557,3.16369049,3.436442348,3.564847615,3.939296881,3.135578324,2.795809966,3.231405409,3.900665914,3.858864146,3.247922242,4.242751648,3.568360588,3.285303592,3.516811707,2.896255486,3.692729498,3.527358014,3.516811707,4.200450485,3.799703235,3.513130914,3.896255105,3.591744578,2.616746197,3.727870383 +DCLK3,2.742034226,1.762810127,2.418838328,2.677307237,2.521370944,2.729027686,2.063096429,2.260364646,3.338361773,3.264748912,3.143146496,2.8741115,4.211035915,3.785810899,3.048947454,2.444296564,2.063096429,2.16133576,2.649279357,3.50057599,2.708429009,2.335997809,2.758276851,3.769567359,2.384262559,2.647232736,2.412338104,2.754323985,2.223146519,2.501162469 +DCLRE1A,3.860489692,4.402382201,3.927027499,2.959232492,4.074887985,4.834973725,4.811022293,4.318091063,4.301475703,3.622600694,4.321118721,2.722304036,2.961455186,2.897485982,2.88450576,4.219210363,4.539255076,4.39129519,3.408324905,3.975818747,3.201322849,4.143388168,3.927027499,4.044471874,3.927027499,3.573806829,3.724872482,4.500151713,3.791013311,3.644954571 +DCLRE1B,5.116104717,5.004078435,4.528180231,5.675489298,5.883828238,5.253422102,5.063755825,5.301414979,5.165624755,4.716354753,3.902529667,4.318180115,4.966676409,4.971602311,4.08326355,4.614985224,5.254725892,5.229266113,5.104766608,4.810516819,5.056773799,4.820878089,4.829512065,5.091386453,5.464989833,4.90182976,5.004078435,5.276648334,4.419813748,4.700512209 +DCLRE1C,5.85411664,4.458107081,5.547555666,5.225371371,5.70306063,5.010993779,4.978723485,5.276912997,3.959459441,4.313986221,4.677869511,2.895517518,3.986491773,4.011434475,4.439364411,6.254482084,5.540625634,5.307150909,5.74929382,5.540224242,5.421928414,5.411384076,4.525433217,4.944053838,5.081222862,5.010993779,5.010993779,4.775833511,5.0709881,3.666645828 +DCLRE1CP1,3.974266336,4.324879294,5.45049006,4.60587512,3.229114176,4.835899149,3.726500913,5.465264426,4.722809581,4.800793989,3.938844647,4.324879294,3.518393222,4.682065781,5.780566467,4.324879294,4.837080924,4.002941895,3.202656851,4.326265856,3.762396392,4.983087441,5.260890487,4.038375334,3.229114176,3.323154884,4.511646881,3.336845415,4.328768023,4.980123429 +DCN,9.631066799,10.15480657,9.539176208,8.749507369,10.71758907,10.60848794,10.5639262,10.39871599,8.887278434,7.492533634,8.173028634,8.638088506,8.680067794,9.725766797,9.240120423,10.05894343,9.012299759,9.123364472,9.212328214,9.557779461,9.351518538,9.980947365,8.586391649,10.3234197,10.7814138,9.738665603,9.553884361,9.649934278,10.1985312,9.56121741 +DCP1A,5.186925982,5.568093603,5.086643838,4.694557985,4.831674841,5.376913593,4.354230323,5.488574841,4.622075101,4.569593637,4.251882506,4.732703753,4.147958263,3.939088936,4.851148614,4.444261378,5.098232341,5.110729767,4.564174673,4.858287853,4.823892951,4.74568465,4.726993435,4.569039538,4.739188248,4.501944097,4.122358568,4.611142026,3.880784564,4.578588416 +DCP1B,4.9780179,5.14098477,4.902097148,3.679048398,5.468109907,5.668826824,5.666578273,5.833823958,4.544829615,5.562814224,4.109573555,4.684059777,3.419759736,4.890166454,4.277322868,4.6704947,4.939566194,4.691967014,5.067927544,4.572130427,4.711071776,5.191827008,5.220262475,5.613297275,5.844839278,5.329546403,4.28433749,5.462969955,4.764722706,5.231100901 +DCP2,5.580555918,5.226221448,6.150508275,4.453168691,6.07896256,6.240513446,5.866045938,5.834596953,4.955785833,5.097626848,6.181581273,4.562710118,5.454297081,5.042662366,4.923066061,6.438833115,6.095328123,5.580555918,5.901673126,5.813617267,5.744798588,5.457365409,4.957394329,5.561286898,5.580555918,5.594485843,5.905059472,5.769162979,5.38501878,4.96814297 +DCPS,5.547126704,4.750856267,5.637444673,4.251566523,5.389798334,5.352421768,5.218546315,5.035562958,4.957669027,4.412842774,5.505971439,4.245869337,5.160421431,5.372508923,4.891304952,4.765090931,5.058263588,5.958573712,5.081126568,5.374388259,5.352453802,5.446315703,4.84834334,4.48821299,5.346957751,5.335110732,4.913211107,5.545627739,4.727915817,4.66118026 +DCST1,4.253005677,4.253005677,4.676906455,3.951881832,4.041788912,3.697386413,4.020237391,3.417489751,4.253005677,4.662856403,4.253005677,5.061556887,4.632156875,3.970290868,4.120073281,3.925504393,3.945333664,4.371854322,4.253005677,4.831587744,4.12519569,4.075962737,4.297642396,4.043553861,3.893014123,4.253005677,4.834756349,3.982205654,4.890221757,4.10434139 +DCST1-AS1,3.92322278,3.834185712,4.30527715,4.241491524,4.387339738,4.243839977,4.855793099,3.953057387,3.834310815,4.299780506,3.929363038,4.639569655,4.455619121,4.203537228,4.119236558,3.423290172,3.989327514,3.846148159,3.38347876,4.29339429,3.631045743,3.717753079,5.351259046,3.415372513,3.988935189,4.360431722,4.33748774,4.034408272,3.51163983,3.016863733 +DCST2,4.246815505,4.397184038,4.783248219,4.77681052,4.581088497,4.470768439,4.170042276,4.438698151,5.679277042,5.556383954,5.272595213,4.606484053,4.839023502,5.341502379,4.356020723,4.434934174,4.249409031,4.141782232,4.112302823,4.617249564,4.261161471,4.581088497,4.565564084,4.814838632,3.84738208,3.948120803,5.041690604,4.277089127,4.402303047,5.763352958 +DCSTAMP,2.48895881,1.989998687,3.170179552,3.670507485,3.083619044,2.020358251,2.399967166,3.015509595,2.939896797,3.109835961,4.228822197,3.291632396,3.157365636,3.493041694,3.202646229,2.555811064,3.391106315,3.083619044,3.265673424,3.390066767,2.410549608,2.984338848,2.634242847,2.85609471,2.691800161,3.399239306,3.835210079,3.182958768,3.083619044,3.210056829 +DCT,3.863693408,4.584956534,4.420205282,5.271258255,5.646298623,4.419702116,4.966401516,4.670660422,5.100549621,3.477221024,4.573917896,5.588545923,4.673546358,4.684761524,5.288750174,4.24432545,4.039904893,3.898960168,3.536446598,4.226302998,4.675665651,4.811597426,5.410670628,4.682287487,5.892796977,4.637859349,4.79211144,5.615529923,5.130795165,5.223345688 +DCTD,6.016719431,6.421006605,5.94719981,4.448137546,6.40814473,5.713512851,5.653751815,5.840407559,4.866525425,4.499715157,4.756163433,4.993965346,4.513095313,5.670615949,5.350140185,5.567303737,6.129000553,5.765854219,5.440295114,5.429583577,5.434114647,5.556016654,5.292710227,6.086426884,6.003413227,5.872847948,6.131551859,5.703357964,4.778615236,5.83576579 +DCTN1,5.047689605,5.42886815,5.384197084,4.779824816,5.511474834,5.195897863,5.662114958,5.138930165,4.602224629,5.203015319,4.596558506,5.293478517,4.526958767,5.01295139,4.824420869,5.631232571,4.882672676,5.655787986,5.299188067,5.192191179,4.935399196,5.679579528,4.910311619,4.976454707,5.182374444,5.302430069,5.533292776,4.787598273,4.518602415,4.435469197 +DCTN1-AS1,3.628956644,2.733411032,4.249410287,2.783463861,3.598188118,2.922296633,3.660856044,3.219265247,4.26219635,3.201044288,3.515444618,4.222219764,3.642983239,3.835484109,3.234755746,2.849173433,2.742026723,3.474114009,4.339080621,4.098380902,3.999730589,4.020315152,2.652753614,3.315146201,3.661425421,3.103300075,4.293873115,3.172276703,3.995114604,2.621408204 +DCTN2,5.790464225,6.1142275,5.067285302,5.148008169,5.842904146,5.982563153,4.888376641,5.69801969,3.902227438,5.143131012,3.921547707,5.388049903,4.599450654,5.557251264,3.925967592,6.277738488,5.009921458,6.185060876,5.527901989,5.325775188,5.441769612,6.160629754,5.530424532,5.259051303,5.438612714,6.370404434,4.986491098,5.271302971,4.701461564,4.992226942 +DCTN3,5.652906435,4.99336572,4.292456876,4.83822521,5.878130196,4.982854976,5.087271168,5.682811166,4.832738183,4.522365501,5.048939453,3.894095434,5.153807768,4.62432273,4.621717706,4.968330995,4.994253915,5.057081446,5.152297566,4.753549712,4.753051964,5.066463474,4.194459597,5.399796187,5.19247143,5.495942402,5.045743501,5.584738189,4.278208763,4.343583362 +DCTN4,5.097292665,5.029193949,5.692788414,4.519140711,5.575944181,5.966197492,5.705515027,6.371176044,4.305266108,4.582040188,5.077600462,4.394383682,4.764782458,4.62097109,4.198919259,5.703980336,5.656376,5.359819414,5.07920679,5.850208041,5.444356453,5.330909927,4.132090209,5.568482152,5.778718286,5.423078127,5.141657383,5.333645558,4.56377737,4.184136007 +DCTN5,5.905647598,5.913048365,5.886444346,4.533299556,5.494398382,5.931416802,5.112388083,6.368100634,5.721382358,5.754894424,6.264081196,5.259149159,5.197666332,6.936318658,5.4486087,6.525131386,6.550469871,6.11070223,5.938944103,6.180572762,5.728630365,6.350644163,5.944510323,5.878781438,6.049745577,5.54919168,5.715479668,5.885616676,5.263217236,5.943931356 +DCTN6,7.456503585,7.471445101,6.766895274,5.376780283,7.56497518,7.498048752,7.377599405,7.614881139,5.628188758,6.087252415,6.517447778,5.726382017,5.726482547,5.432824355,5.916789682,7.136438258,7.252578866,7.416255453,7.561129055,6.689981316,7.509948555,7.59042806,7.013913535,7.872201469,7.703864317,7.652148103,6.054883281,7.229607147,7.10670934,7.13013961 +DCTPP1,4.68156147,3.881643603,4.78741681,4.253298222,5.328484898,4.883627789,5.2506271,5.233498128,5.300678427,4.896574882,4.934850008,4.76391644,4.675944116,4.861708918,5.118722457,4.243063125,4.658877202,5.088342923,4.595136851,4.87218477,5.18317796,3.99867082,4.773716865,4.37406774,4.849075345,4.716577024,4.774064514,5.167402928,4.739167977,4.817847327 +DCUN1D1,6.911796165,6.707891898,6.306076422,5.720865568,6.518360644,6.682098935,6.956653786,6.80497475,6.084543845,5.588019577,6.182072956,6.542520346,5.478701891,5.788862073,5.552635854,6.797787627,6.201603215,6.78929516,6.695171542,6.552375105,6.191011169,7.107298195,5.86029666,6.798890494,6.51062601,6.729645104,6.922278864,6.541138617,5.599124915,5.983249007 +DCUN1D2,4.436947271,5.140438094,4.952464493,5.17784626,5.546321198,5.037476188,4.866813001,5.053065607,3.862169674,4.408234875,5.008018798,4.78238504,5.181787303,3.502971041,4.298179489,4.716833366,4.14777683,4.445866689,5.124083665,5.069699016,5.000757332,4.737716938,4.574364313,4.489191515,5.617013957,5.526509454,4.936905927,5.237627213,4.879825172,3.753872408 +DCUN1D3,5.174977431,4.635030669,5.127239241,4.819096704,4.559592751,4.648947834,4.511551481,5.353563848,5.077323813,5.338648344,4.279834287,4.699544408,4.80104497,5.100035056,5.439983374,4.675010274,4.281566235,5.800658267,5.35993419,5.107152559,5.227195948,5.190705628,5.07273116,5.147297505,4.546452622,4.951035199,4.989301175,4.442129316,5.158229759,4.732531458 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+DNAAF5,4.377220397,4.23496934,3.807816432,3.97538257,4.358767017,4.341836429,4.234969264,4.407508821,4.025386701,3.795967835,3.687985723,4.61224405,3.731869833,3.981391349,4.070518742,4.357365009,3.473813216,3.824472126,3.534111517,3.546569538,4.143610361,4.615290027,3.682869229,3.716542876,3.964321472,4.419025201,4.178685436,3.31710447,4.007488136,3.202465933 +DNAAF6,3.46883045,3.270520703,3.53072377,3.286911037,3.451239032,2.639806198,3.209479028,3.441591479,3.321596749,2.470459821,4.143734575,3.754108647,3.240882687,3.461525609,3.647044823,4.034955249,3.517129381,3.303490251,3.210486757,3.147565767,3.547529855,2.726412359,4.027727734,3.469126622,4.060198347,3.303046701,3.954170462,3.052882374,4.049181714,4.431526399 +DNAAF8,4.303432377,3.687984173,3.953871995,4.251582479,3.990710364,3.470334658,3.692963091,4.24603797,3.691355512,4.603908243,4.442900684,3.595765587,4.715716157,4.021537731,4.529848659,4.096693711,4.122829155,4.202689583,3.366972711,4.048290737,3.803977156,4.047527275,4.004021523,3.647045397,3.78346917,4.153443942,4.258150513,3.867782578,4.431621326,4.364137922 +DNAAF9,6.039451574,5.92314295,4.464164198,4.950491957,5.848687304,5.999266553,6.0017826,5.872671927,4.304403867,5.224137542,4.584772246,5.200507425,4.633608675,5.204501025,4.712830257,5.923203278,5.254440722,5.757416172,4.991993631,5.527262876,5.604779755,5.410922834,5.012879993,5.960301299,6.179966262,5.76567443,4.548419449,5.602682635,4.908357457,4.332047581 +DNAH1,4.219720766,4.450210329,3.792246431,4.416982859,4.42370131,4.501077331,4.516596034,4.42400419,4.232853909,4.637900967,4.905901546,4.398189304,5.153140354,4.795444659,4.270006741,4.051202306,4.413238853,4.785554902,4.394151419,4.800098256,4.534371301,4.394977462,4.453242774,4.21496203,4.288253373,4.284650486,3.985103193,3.94045146,4.247189374,4.420303647 +DNAH10,3.256733093,3.515193613,3.624627464,3.511242109,3.306922423,3.089885928,3.107210594,3.09754227,3.525396557,4.045420774,3.937129141,3.525396557,4.01373237,3.924358276,3.654579176,3.274911252,3.494165956,3.525396557,3.636406852,3.755535922,3.649213643,3.219782715,3.923230537,2.877345807,3.443631951,3.403405687,3.525396557,3.503837149,3.973616016,3.999439795 +DNAH11,3.563234804,3.483996472,3.400192874,2.843870892,2.911407929,3.272775834,3.305886873,3.697388748,3.67673828,3.321124054,4.094298946,3.585219516,3.608859686,3.400192874,3.545545186,3.581422823,3.323519987,3.243901431,3.398210978,3.632163767,3.126573851,3.477680765,3.62938958,3.224835191,3.560815357,3.362790848,3.558044825,3.109368437,3.339226493,3.375829588 +DNAH12,2.669903953,2.767730021,3.781482874,2.969623494,2.363502183,2.262976292,2.554335899,2.348414765,3.206098158,2.990550879,2.607741725,2.716526289,3.140545516,3.061573876,2.732215854,2.42448159,2.752157686,2.583241581,2.738829454,2.386854691,2.691713091,2.528961353,3.181303092,2.50314418,2.507633552,2.639800956,2.748085491,2.799653466,3.360843648,3.116001033 +DNAH14,2.978090135,3.418685268,3.034492274,3.17748907,3.113778386,3.087341234,3.052144493,2.605722427,3.375820382,3.182447991,3.323196474,3.14756238,3.376992243,3.36610556,3.198422597,2.942382761,3.250984623,2.766528984,3.602592277,3.147974565,3.164113855,3.201975066,3.521893629,3.06095847,2.85110622,3.041220472,2.989608336,3.916489298,3.366035178,3.174537135 +DNAH17,3.225502389,3.101478007,3.133747815,3.28500471,3.234453845,2.874905689,2.811438394,2.341352823,3.107821028,3.450643579,3.025671136,3.69188791,3.403715347,3.107821028,3.111371996,2.838763544,3.107821028,2.656284593,3.162403507,3.562303832,2.730285467,2.912154704,3.274890269,2.95486777,2.818547917,3.585907542,3.523430703,2.779446745,3.240902516,3.277269726 +DNAH17-AS1,3.90914218,3.102207423,4.37135803,3.526410711,3.485063756,3.296072646,3.506385319,3.375579723,4.891063265,4.082766257,4.29167447,4.528672576,5.12897869,3.824327076,3.734043159,4.042778515,4.454838287,3.846274116,3.327672622,3.599676178,3.731462642,3.454333588,4.831694322,4.428883758,2.825379356,3.499282311,3.882668022,3.846274116,4.715274082,3.59724428 +DNAH2,3.328389303,3.532676862,3.945573022,3.672978135,3.141040304,2.840715446,3.564203819,3.310802154,3.781880131,3.987041533,3.559588775,3.858845075,4.185089383,3.504279797,3.438813754,3.145052515,3.415896443,3.424046903,3.351100123,3.437608943,3.287587276,3.164872339,3.840160743,3.532676862,3.213777334,3.508181218,3.497950266,3.259664734,3.531779602,4.145947676 +DNAH3,3.231534136,2.687101984,3.085448835,2.916202983,2.710094779,2.77764349,3.246922384,3.065711647,3.520357505,3.065711647,3.890857953,3.394568165,3.288578075,3.150877333,3.193648168,3.033317229,2.77764349,2.624807383,3.209301327,3.117496382,2.808132013,2.404163766,3.457025699,3.065711647,3.440656245,2.636040286,3.580307735,2.805559135,3.152351745,3.666838592 +DNAH5,3.405579648,2.750710557,2.836623504,3.672964527,2.848860717,3.177889967,3.32457846,3.273509443,4.056427773,3.347038104,3.351804199,3.436924486,3.938879707,3.439311624,3.6565628,3.462819164,3.008214159,3.188687509,2.822007904,3.279788424,2.893686413,3.173730551,3.650948131,3.448628408,3.174565263,3.491311659,3.347038104,3.635355457,3.918619344,3.347038104 +DNAH6,3.349904359,3.255789067,4.009891023,3.255789067,3.293721593,3.039160658,2.965792657,3.16893521,3.000413144,2.990799861,3.255789067,3.458346767,3.56141631,3.173337118,3.413657202,2.924538859,3.239339683,3.255789067,2.864836091,3.255789067,3.142123133,3.073570836,3.777347979,3.257456154,3.12094704,3.284720361,3.048848104,3.865478522,3.807445936,3.793515191 +DNAH7,3.998454638,3.644346644,3.504049008,3.644346644,3.571679873,3.614515472,3.315684514,3.51774275,4.355707588,3.784455708,3.060162639,2.87631543,3.834140839,3.677583482,3.62087453,4.081352641,3.210751222,3.480321192,4.031941124,4.124805623,3.644346644,3.304049755,4.344454499,3.466267862,3.786503543,3.577000771,3.862643895,4.244658194,3.360392643,3.644346644 +DNAH8,2.67083427,1.983694304,2.970329168,2.649556444,2.235177699,2.390480588,2.14009006,2.3780248,3.466283606,2.556713432,2.853799548,2.541723775,2.474543187,2.736760841,3.158610215,2.349582179,2.648963219,2.645518534,2.574130674,2.809033688,2.648963219,2.246382847,2.653603595,2.656289638,2.605294201,2.54777453,3.5820038,2.70460513,2.468509228,2.604635815 +DNAH9,3.766811221,2.73010528,3.471271005,3.480106697,3.426319564,2.723719048,2.944218025,3.271589833,3.386661012,3.919327802,3.974465648,3.219625986,4.290472062,4.072830976,3.396624124,2.972765464,3.073108092,3.266373752,3.465221447,3.334503586,3.386661012,2.758425376,3.79134843,3.16587782,2.972765464,3.359264508,3.582390675,2.997415913,3.5197425,3.616196839 +DNAI1,3.643646129,3.593109236,2.766265735,3.616032178,2.755836915,3.32097851,2.32669769,2.656120497,3.458322985,3.54112733,2.923976977,3.555065381,4.02357737,4.298884817,4.335979154,3.984149274,3.532411997,3.735870893,2.658419499,4.493981295,3.181248269,3.28978465,4.084949251,3.736597969,4.232294781,3.3141879,3.832653396,3.282482395,3.858830183,4.809017245 +DNAI2,4.048959502,3.536640893,4.162261266,4.793592573,4.044991245,3.277050575,4.172701374,3.547830562,5.392514071,4.256389381,4.506010579,4.610502943,4.871428918,3.901399075,4.131757145,3.858228776,3.915073768,3.705459796,3.88787953,3.863639988,3.335605325,4.505600492,4.046212572,3.732788714,3.666702097,3.940193658,3.965717992,3.895125265,3.676540337,3.965717992 +DNAI3,4.593259758,4.593259758,5.362025724,5.090153552,4.839808818,4.798953286,3.879268432,4.260000531,3.784182466,4.118718792,5.423307753,6.040364716,4.361566323,3.876642686,3.967082339,4.319497254,3.605853745,4.261019263,3.897700861,3.085107451,4.593259758,5.409314968,4.1448261,4.769592423,4.758814688,3.861453847,4.792738163,5.521132886,5.274308288,5.179563688 +DNAI4,5.168068734,5.518682106,5.348993131,3.985765737,5.596341981,5.448912067,5.311346091,6.395176325,5.403930824,4.919707189,4.173343101,3.952515774,6.007799778,4.483718003,5.228655577,5.535287018,4.981380473,4.932724876,5.542971106,4.285105723,5.004092807,5.623873285,5.938707842,5.168068734,5.377299065,5.273053412,5.480395053,5.618914369,5.55290099,4.755467947 +DNAI7,3.343952239,3.076019963,3.393670291,2.665042628,2.976336995,3.382465693,3.603177471,3.439067757,3.916077948,2.831338448,3.296033205,3.202720864,4.004890444,2.632896084,2.924435733,2.962820294,2.610631669,3.16328938,2.606645359,2.589312819,3.111745149,2.629673868,3.220801584,3.233757168,2.174372723,3.586643726,3.800079695,2.805865919,3.115308762,3.230013871 +DNAJA1,7.781493309,7.217426446,6.038192958,5.506567506,8.210576656,7.64634909,6.799407427,7.62779796,5.215198898,4.654626368,6.676951378,3.291989597,3.067427403,4.313454875,4.643986894,6.115822573,7.661760215,7.484742821,7.669068734,7.26913397,8.090979056,6.671588595,4.071825446,6.495515345,6.966733922,7.337017707,6.684577669,6.689609776,5.981753636,7.193946354 +DNAJA1P5,4.75412289,3.902611881,3.742775578,4.015761019,5.196023209,4.608832213,4.784741711,4.871034995,5.937949058,4.790665636,4.717227415,4.625251208,4.02666999,6.570817114,4.174589192,3.637941212,4.791752975,4.75641972,3.805379725,3.786958604,6.534960233,3.497283905,5.255952374,5.972168806,5.864856663,5.134693509,4.245475115,4.079343321,4.223848781,3.731710756 +DNAJA2,6.192246643,5.815397311,4.472421868,4.322858402,6.721782991,6.842766879,5.982335129,6.730464172,4.958153707,4.927379201,5.296916802,4.668688377,4.771354804,4.831459348,4.909455677,6.223983187,5.319694333,6.291318303,5.899531397,5.478871712,5.768473288,6.05776108,4.851442008,6.083752129,6.129822296,6.522247067,4.466943801,6.143473478,5.590856476,4.422243905 +DNAJA3,3.784101358,4.203378926,4.092059715,5.078704098,3.83581991,3.928701165,4.853859765,4.419407166,4.418015438,5.202017616,4.404903561,4.508593156,4.778483419,3.716857023,4.956339063,4.625778069,4.653043287,3.992778422,4.2520466,4.366539517,4.354577117,4.534538201,4.883275731,4.126590158,4.00043111,3.985010763,4.920255977,4.392387592,4.432943585,4.530179856 +DNAJA4,5.948707746,5.533154475,6.567039298,6.213242409,6.192520566,5.426185294,5.489787691,5.703249921,5.325901966,5.901665876,5.889349792,5.696286265,4.912427263,5.789898858,5.899233734,5.586693864,5.345551912,5.942631762,5.718724491,6.518001551,6.134262441,6.82310856,5.839496215,5.63771011,5.442417405,4.968477179,4.71822787,5.899587872,6.12778043,6.448037842 +DNAJB1,5.396616153,5.636802168,5.687427069,4.795689757,5.996679444,5.652891469,5.998987096,5.515354019,5.048888293,4.259349669,5.209736875,4.866756421,4.740453271,4.742996232,4.869510182,5.491003188,5.397533637,5.992784568,5.555649995,5.135454311,5.415983768,6.127546074,5.084071709,4.869510182,5.54604511,6.464734082,4.755345095,5.025958804,5.551186571,4.590071348 +DNAJB11,7.176515352,6.56579318,5.719239097,5.215473045,7.623646593,7.20883131,6.439560828,6.898596429,4.108297223,4.785491712,5.844422689,5.66295803,5.28990123,4.502707839,6.133021838,6.082876459,6.090246914,6.555481301,6.139768873,6.190303908,6.844889893,6.774280945,5.598415568,5.969122283,6.599627048,7.081537443,5.023863676,6.30476648,6.036172344,5.676889552 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+HOXB9,5.25119921,4.862061837,5.495079441,6.279297039,4.112234053,4.757821494,4.174289737,3.790730993,5.162761388,5.925959286,4.797191197,5.681422472,5.800147047,5.342620412,4.934672605,4.673707389,4.398378654,5.054860062,4.782553608,5.37727874,4.090999599,4.636718789,5.157280513,4.083422092,4.549217782,4.935779342,5.417264838,4.338695437,4.582002135,4.968692926 +HOXC-AS1,4.878919478,4.462566391,5.259573555,5.047831759,5.074227026,4.86870221,5.714740252,5.138883174,4.4759029,5.537795217,5.536770631,5.020461299,5.55365434,5.55986438,5.676726449,5.038001794,4.553065013,4.080843803,4.337464493,5.172775784,4.476302968,5.337281222,6.397875869,5.840161313,5.125985324,4.340283334,6.108945613,4.987682312,5.062051908,4.83782368 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+LRP5,4.30402485,5.138795789,4.727196385,5.067818479,5.428288778,5.01987521,5.38634135,4.916683149,4.487622657,5.096097902,5.655753547,5.018275458,6.738387472,5.786495863,4.910725544,5.111766612,4.592267086,4.72787823,4.375109451,4.542521019,4.724570185,4.745221944,5.190099983,5.275488359,4.637239697,4.799376691,4.956716799,5.193807345,5.102820793,4.276317255 +LRP5L,4.634753595,3.9161481,4.638160143,5.728574087,4.317665536,4.64578729,4.345156599,4.646284315,3.770389231,4.163561578,4.309893026,4.503597082,5.759985295,4.639567597,5.244131668,4.050527631,4.720321352,4.201458259,4.848012583,4.320628086,4.386709333,4.413248442,5.038341336,4.573822892,4.372304095,4.848659481,4.725527959,4.142949413,5.242145923,4.71565186 +LRP6,5.897217979,5.429796089,5.962915245,5.925840997,6.286293604,6.089874022,6.035580586,6.629216709,5.556904377,5.755668361,4.854935885,6.411186943,5.092412973,5.974695178,5.873102969,5.865704686,5.86581718,5.553602436,5.96618824,5.825151214,5.917807198,5.735906118,6.099372143,6.263837602,6.00893954,6.221750346,6.022452795,6.440521595,5.922693189,5.520780871 +LRP8,4.632022924,4.373481454,4.516666189,4.570113096,3.905512528,4.543327532,4.555008993,4.207926525,4.146249493,4.78471614,4.327059089,4.951139128,4.685033097,4.049522834,4.563643359,4.438916505,5.615499087,4.594327931,3.970437758,4.995646895,5.005130944,4.9815305,3.918892387,4.709341966,4.431144826,4.591842491,4.570113096,5.232745555,4.32423716,3.976225904 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+LRR1,4.850214527,4.192281322,3.97943861,4.458568033,4.957636496,4.335878742,4.39479755,4.464740525,4.421827293,4.661264983,4.972921218,3.686033581,4.48958731,4.63111297,4.426156654,4.479379183,4.548867058,4.721186851,4.887655641,4.832831836,3.635030308,3.578294171,3.858086759,4.88321803,4.418473664,4.656150966,4.38731509,4.293968059,4.58079699,3.970589878 +LRRC1,5.700874168,5.535527009,5.931782283,5.572463914,5.66438782,6.019148791,5.746403168,6.338231689,3.936935387,5.354469848,4.919638503,5.183124769,4.619062611,5.338744506,5.412074795,6.421671423,5.544736155,5.435084392,5.791522401,5.660263636,5.247301228,5.601900752,5.222284735,6.1332472,5.845687991,6.224453872,5.963523382,5.55278034,5.326729426,4.825461869 +LRRC10,3.659356055,3.880759776,4.505819784,5.357330497,3.933952228,3.926245494,4.202087491,4.188422817,5.288198899,4.895700359,4.620768083,4.779916774,4.975901622,4.215888692,5.345639428,4.201002336,4.327941159,4.085953493,4.248850575,4.589480529,3.802174817,4.342674857,4.436069104,4.012992852,3.748860149,4.440286566,4.058881083,4.193011652,4.75587033,5.029789402 +LRRC10B,4.430073149,3.853942605,4.680209913,5.211208787,3.171296695,3.203903116,4.100986835,3.26090507,4.963198429,4.113097084,4.188680193,4.234761594,4.88876443,4.861985875,3.677938383,3.429117389,3.551357351,3.278622658,3.474505688,4.373609944,3.892740155,4.057350385,4.698860093,3.005799035,3.419923209,4.147503528,4.757860785,3.872071315,4.467817285,4.250184883 +LRRC14,3.944618881,4.22404239,3.922371337,4.431685336,3.762868729,3.530426333,3.794190286,4.00815587,4.805417916,5.153333207,4.455008119,4.926817979,4.715860013,4.30062361,4.617485204,4.426806437,4.757548839,4.180885509,4.439976801,4.30062361,4.374280804,4.319193305,5.060832226,4.059377821,4.271071566,4.491561896,4.827604899,3.984718177,4.481030124,5.45527307 +LRRC14B,3.742664536,4.674766792,3.91903716,3.598029511,3.536653686,3.012288474,3.171146173,3.683275816,4.548353356,5.124990634,3.956373241,4.672297238,4.17799633,3.554049417,4.308558537,2.80693397,4.26472239,2.923335378,3.70955491,4.064677198,3.35937025,4.670308408,4.555856483,3.96254118,3.772946494,3.960168243,4.015820291,3.916220581,3.976229171,3.83686765 +LRRC15,5.122527291,5.970468855,5.513280058,5.086157696,6.126920321,6.202567781,6.158200796,4.23056443,5.605763209,3.871399152,4.962975933,4.476568975,4.168548086,4.860810334,5.256624568,4.850065482,4.998336165,4.597096037,6.197503468,5.549205157,5.379196683,6.569346261,5.750137629,5.089341997,6.381719647,5.446686994,5.421320117,5.379525795,5.100763789,5.567154768 +LRRC17,3.346310889,3.668951683,3.442035128,2.679627731,4.777966814,4.178128881,4.296180674,4.593582089,3.609654436,4.000812066,4.744130815,3.61226159,4.00245755,4.552299352,3.135051258,3.149654511,3.936548254,2.801073741,3.881396604,3.892209275,3.101097358,4.402409428,4.335373389,4.906750175,5.402707002,4.40017952,4.523871763,5.374810497,5.724307892,5.194146132 +LRRC18,3.554966501,3.200882373,4.464958,3.699401859,4.758342247,5.098948775,4.4076722,3.159860069,3.038496352,3.912203595,3.853457366,5.210059612,4.524230396,3.903786832,3.249959122,3.495581999,5.274576431,4.849461599,4.896516265,4.554312634,5.067006572,3.787992582,4.340799206,3.511728965,3.46127358,3.864372722,2.945084192,4.162131975,3.908301385,3.105754418 +LRRC19,2.983479243,3.199097463,3.180277803,3.662316637,3.156845066,3.978253157,2.901173729,3.408175885,3.25373182,3.76113973,3.078467729,3.277326062,4.182765028,3.477595522,2.462719686,3.737694381,3.659158524,2.736004789,3.617767418,3.30754348,3.532788812,2.879750243,2.95448368,2.874651619,1.993645258,4.436796095,4.176414766,2.973322959,3.107416513,3.607339393 +LRRC2,4.584716159,5.255724026,3.778465353,3.91026374,4.323093591,3.449115803,4.046642955,4.051909447,3.083461624,3.565146827,4.392895776,4.345396411,4.082837669,4.910219804,4.418508156,4.133232117,4.348745238,3.886924696,3.882166,4.059401436,3.740203172,3.781220624,4.688618077,4.273311813,4.014085504,3.264365718,4.080575784,4.286006495,4.909210736,4.024715739 +LRRC2-AS1,3.853490751,4.0486278,4.510765648,4.300446172,3.673292071,3.751500934,3.773078516,3.228610507,5.032227691,4.368854497,4.80148665,4.722842765,4.4247928,4.06153685,4.135355142,5.157380383,4.141756371,4.300843636,3.467563284,4.729311354,4.171820533,4.135355142,4.065618795,3.59387667,3.527583416,3.852746355,4.030313901,4.539979697,4.135355142,3.42895507 +LRRC20,4.491087283,4.806095617,4.727796554,4.797396076,4.194065699,4.100776664,4.238615475,4.298110851,4.679537029,4.969917285,4.876002981,4.331937898,5.077926661,4.991931617,5.041105224,4.866778404,4.37414995,4.077824852,4.450157122,4.187056567,4.580311681,4.644795049,4.022311449,4.151976956,4.146847154,4.390410803,4.171031688,3.752064892,4.271842322,5.154412001 +LRRC23,4.173415414,4.083812493,3.634193422,3.180950938,3.916396237,3.823720793,4.490116807,3.835116687,5.212085876,3.416676811,4.701622272,4.209798076,4.321444863,3.688902206,4.615928595,3.744276028,3.104793588,4.097642046,4.187261463,3.269157721,3.189912302,4.272878771,4.182297549,3.894189627,3.669367321,4.489976716,2.873259849,3.078887734,3.644715044,3.670645804 +LRRC24,5.133876886,4.354193439,5.743438112,5.015818738,4.480681939,4.419818142,4.842251174,4.313249093,6.021557906,5.215168964,5.635640613,5.749902268,6.544493873,6.817402744,5.165434462,5.162197765,5.16654007,4.522802725,4.738573574,5.339215154,5.508197846,5.028894496,5.727507058,4.948187617,5.18894458,4.365461007,5.828872363,5.357025636,5.335546045,5.818347137 +LRRC25,4.258800696,4.350737249,4.45588544,4.245470634,3.709636232,4.061690507,3.436442348,3.941984107,4.385536473,3.992507922,3.721989834,3.677172809,4.232899495,4.206157537,4.656800324,4.324879294,3.772956082,4.809686988,3.674495523,4.603336103,4.583213325,4.321580088,4.078402341,3.541953591,3.63786793,3.665852956,4.117705865,4.061690507,3.77137987,4.010154127 +LRRC26,5.441957604,4.808563244,5.018395182,5.020616463,5.132302606,4.029512044,4.934165646,5.373319711,5.766805476,5.810429009,4.389769733,3.450263809,3.911262693,5.665185095,5.349500485,5.692125469,4.825603314,4.877008335,6.211983899,4.140201399,4.934687809,4.300563298,5.715431362,5.141843673,4.454795849,4.015930505,4.078904244,5.778550634,5.181174575,6.348079637 +LRRC27,4.090795646,5.026900045,5.68066012,4.372888947,4.867056419,4.833397509,4.506373917,5.191627413,5.144232162,5.077007362,4.922674791,5.640650823,5.028425098,5.416256145,5.535821739,4.936309302,5.372122576,5.190928679,5.117990161,4.672911332,5.72248414,5.078455122,5.845154223,5.343822308,5.270073707,5.04463972,4.323530552,4.656391806,4.85522969,5.760191272 +LRRC28,4.743944154,4.812691082,4.619546989,5.052970677,5.348069921,5.178314368,5.161612744,4.776275415,4.453779829,4.933968381,4.353936875,5.169187637,3.43416812,3.926935829,3.81999482,5.118883204,5.063362535,4.937998486,4.989442471,3.683502507,5.071975825,4.535822438,4.499919224,4.965211809,4.760135584,5.129144164,3.453509048,4.68694871,4.811411767,4.544212122 +LRRC3,3.520829113,3.810336719,3.895006752,2.587167083,3.839016354,3.702468414,3.618584668,3.929531218,3.969749526,3.986488526,4.475060389,3.656304185,4.175181725,4.246924745,5.00835002,3.468024788,3.715693283,3.961892202,3.913398213,4.167717921,3.772174135,4.047310048,4.363012404,3.304456645,3.542168632,3.845552934,3.953927632,3.347229929,4.089168201,4.487988824 +LRRC3-DT,3.599861518,3.599861518,3.27750783,4.320757861,3.080594791,3.320110484,3.45538667,3.810278265,4.226230845,3.305431983,3.636931821,4.604947226,3.253282003,4.607421166,3.693543019,3.726916323,3.599861518,2.948756018,3.907171548,3.168988915,3.951636311,3.269634222,3.326513079,3.203726565,3.595721959,4.268518635,3.599861518,2.987120516,3.579558914,3.814812072 +LRRC30,4.106082176,4.080471489,3.262111649,4.733885062,4.04321618,3.785810899,4.142110689,3.970870716,5.585301937,4.175231031,4.386656275,4.609752664,4.54709278,5.716106698,4.830218908,4.117389339,3.862396775,3.516058725,4.225407153,4.402607545,4.915949851,4.133660551,5.367817859,4.524283515,2.749466645,3.829879156,5.491614324,4.649913218,5.044409646,4.034995477 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+PSMD1,7.204922722,6.593201533,6.47412291,5.24935728,6.964296516,6.690374093,6.667864664,6.925999996,5.081602359,5.533002431,6.368447515,4.583980838,4.86807516,4.503086318,5.898630156,6.436325097,6.450924555,6.893132576,6.798061299,6.116333042,6.256288892,6.845452086,5.064676595,6.909831123,6.612529853,7.187459566,5.774739282,6.803015488,5.205068375,6.132042215 +PSMD10,5.776335381,5.479786045,5.282479959,5.164137465,6.27295989,5.885966451,5.661719028,5.979217559,4.34058855,4.839447757,5.278769187,3.924520971,4.41179362,4.556347921,5.789786725,5.401676229,5.721184522,5.297015903,6.754026134,6.190357879,6.250863911,5.867428567,4.749253232,5.752542196,5.738587969,6.043821875,5.401676229,5.466594171,4.506853591,4.73965639 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+PSME1,7.660594455,7.081302366,6.707874868,5.43661534,7.356017843,7.081981519,7.022536156,7.283769551,3.705483779,3.982685256,6.432199077,4.959446693,4.539665709,5.575460557,5.735056361,6.575078076,6.977155387,7.417648453,7.146416842,7.084824515,7.181004175,6.768105047,5.307758723,6.679476752,6.662145431,6.893451462,5.622248787,6.015707782,6.144509477,5.32478122 +PSME2,7.673885053,7.893324532,7.799918631,7.735860186,6.822317286,7.494115495,7.191677727,7.609433719,6.972604447,5.713892399,7.004332358,5.169638385,5.653550734,5.907907597,6.526466723,7.0017265,7.139115107,7.846518276,8.146452735,7.728043924,7.715401278,7.004332358,5.046017898,6.689309128,6.742247738,6.663149031,5.528198541,6.347649823,4.743008792,5.098584734 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+RCBTB1,4.712239461,5.021110948,4.326526609,3.513392862,4.954716641,5.038558442,5.286532258,4.865070416,4.290990332,4.376885494,4.805005301,3.685187148,4.50950653,3.759249407,4.226352894,4.033736878,4.744449645,5.229835153,4.584371175,4.564555162,4.433238468,4.402593584,3.878848417,4.85326892,4.558967058,4.524386833,4.394400576,5.113795804,4.253428833,3.868852049 +RCBTB2,4.072182105,3.790609885,4.048371987,4.719309281,4.391981448,4.289398971,4.271379703,4.108299521,4.279715272,4.47032688,4.394994335,3.887830948,4.258677011,3.812462236,4.812751433,4.752717467,4.23477812,4.704443774,3.792479443,5.018649267,3.701031458,4.441626636,4.514176603,4.437248574,3.863554173,4.271379703,3.73215226,4.494699338,4.216334559,4.075244299 +RCC1,4.335268647,5.731288072,4.289190185,4.242038533,4.803149479,4.756011065,3.821353183,4.584374318,4.048428702,5.360526868,3.810396118,4.652517553,3.598440032,6.18241171,5.510693461,5.27139024,5.734125249,5.154655456,4.822201031,5.609335262,5.394248934,5.140168861,5.53938497,5.38717275,4.462967387,4.693568577,5.241020131,4.092867733,5.169301984,4.086263355 +RCC1L,4.618956631,5.100014332,4.685567699,4.727244863,4.74735999,4.267768028,4.646257426,4.869654415,4.937664692,4.687166872,5.090772781,5.470340477,5.176769468,5.112051643,5.216198783,4.935123747,4.780656433,5.140459815,4.355709517,4.688312973,4.901424807,5.335218414,4.9883006,4.899374351,4.503693174,4.366411277,4.869654415,4.785868849,5.349792638,4.65220613 +RCC2,6.301242795,7.252485279,6.88813186,6.626722064,6.812150111,6.643071754,6.909991467,6.624136832,6.236062618,5.686736717,6.310905562,6.432968633,5.601657692,6.337293461,6.310425723,7.004230421,6.825037751,7.165986887,6.828158792,7.016612468,6.846574245,6.817418586,5.731403902,6.941853604,6.735991265,6.447650105,5.695069688,6.063222673,5.930389726,6.193281919 +RCCD1,3.342646704,4.03036014,4.593709517,4.811162642,4.760914405,4.214235681,4.133488258,4.932553307,4.676866024,4.391066476,5.419057879,4.891878345,4.901816989,3.922912328,4.46992548,4.208094778,4.46992548,4.458223876,4.102009938,4.661358058,4.547992603,3.898735675,3.737032156,4.322933255,3.622563099,4.588860537,4.844382869,4.586811418,4.274032153,4.652918376 +RCE1,3.654707596,5.453846525,4.767333955,2.598029953,4.295251112,5.248299959,5.094047598,5.05068884,3.605537799,3.131481862,3.351804411,4.591842491,4.278015347,3.698552123,4.313986221,4.079718722,4.86627912,4.570602622,4.022455841,4.845960312,3.885310405,4.598600157,3.704992527,3.664968568,4.682395401,5.205272835,3.513324127,5.122001239,4.091102715,3.252229768 +RCHY1,4.194882619,4.753171366,5.277439799,4.079693625,4.408330587,4.923530399,4.467641987,5.040187883,3.493085249,4.345267456,3.653821441,3.630005682,4.039644878,3.321939803,2.674926676,4.300874858,3.614871277,4.425317951,4.714430919,4.089394331,3.456706722,4.56678756,4.521853759,4.956645998,4.76276673,4.092997875,4.078072158,4.17134037,4.128811984,3.608622811 +RCL1,5.584024579,5.437417505,5.603467399,5.035340496,5.94203338,6.228731385,5.620890091,6.632687442,4.124876915,4.217186822,4.82641606,5.466656101,4.230338268,4.595283398,5.269557997,5.179878989,5.34837169,5.152587429,5.098037451,5.367477634,5.863339648,4.975718633,5.119158128,5.388215665,5.95518388,5.92226246,5.360022751,6.266649636,5.714118654,4.178887836 +RCN1,7.051343479,7.066276859,5.710877656,6.436587264,6.03702066,6.951801975,6.579183646,6.680990611,5.342043849,5.565181802,6.814152473,5.282632878,6.688365274,6.392066266,7.684026174,5.887482049,7.346880554,6.651325608,7.121186701,7.350360123,6.440667742,6.733438082,5.665984399,6.508713426,7.100346927,5.942808387,7.522885817,6.607745649,6.443615267,6.055175349 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+RCOR1,6.987482055,6.636732281,6.156144436,6.336473364,6.621346714,6.355592541,6.571833699,6.883662899,6.632217809,5.231371215,5.91251502,5.841295373,6.725141158,5.979662859,5.452148272,6.910050744,5.849571947,6.489469682,6.370945571,6.401298446,6.732156489,6.455446406,4.522334744,6.403747711,6.290239191,6.787240763,6.690813996,6.442283676,6.176172085,5.27887818 +RCOR2,4.163241576,4.260717474,4.609457339,5.524749784,4.708958599,4.857057666,4.715359596,4.197730633,5.179450113,4.53638348,4.821147362,5.304910185,5.649222229,5.181428005,5.124791954,4.641738271,5.571944808,4.471950308,4.753668544,5.468255188,4.725417087,4.744903,5.347736959,4.96991224,4.590698671,4.5100188,5.497998224,4.985640463,4.906815156,5.794134833 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+RDH10-AS1,3.887636004,3.330820126,3.181784252,3.716765871,2.66674471,3.415316751,4.165597077,2.70319255,4.497833009,4.073003311,3.551083169,3.33123957,4.530345923,3.783902656,3.400656933,3.256021704,3.075556886,3.028786492,3.657648598,2.452338863,2.755564279,2.356116913,3.673548908,3.655736925,3.775204131,3.693738473,3.872660529,3.855948699,3.955840248,2.346409266 +RDH11,5.604322492,5.964789358,5.434957234,6.002506419,6.011707823,6.252562416,5.986154796,6.149717271,6.033766214,5.927235397,6.066933212,5.469669606,6.420498373,5.347746687,5.611211592,5.902008888,5.954370917,5.945009016,6.121112604,5.387085212,5.348768908,5.702714086,5.827240689,5.889433458,5.958365334,6.692213928,5.783765092,6.460887356,6.48764513,5.619878588 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+TEX10,6.643194353,6.561019815,5.976109142,5.618776032,6.902461536,6.705802186,6.679259423,6.905932458,4.91147581,5.189910677,5.009490737,4.690969288,4.042674042,5.95217606,5.218844748,6.735541267,6.637695154,6.521183472,5.762891161,6.43564927,6.464324536,6.593191922,5.734003636,7.026845001,6.850474876,6.814226675,6.536148571,6.477478944,5.980735978,5.670120911 +TEX101,3.989631614,2.726009415,3.427153536,3.336482819,2.511489598,2.218186317,2.962378136,2.340579161,3.341094233,3.026734929,3.713200336,4.011242539,4.434038057,3.357252413,3.847009973,3.571652261,3.098156568,3.648337646,3.05779889,3.20947641,4.049661176,3.25223772,3.5970168,2.722699696,3.311700803,4.292090552,4.048629977,4.01040709,3.343690352,3.701935098 +TEX11,1.939344079,2.235517391,2.335154754,2.195915454,2.116981171,2.464789776,2.619174565,1.583271842,2.777913249,1.78452439,2.247226414,2.268184856,2.601783755,1.950917911,2.557395032,2.321987662,1.802121762,2.071287755,2.321987662,1.977583087,2.206040529,1.756879162,2.562885277,2.80952193,2.469493634,2.937630899,2.302433979,3.070483198,2.253814417,3.021105037 +TEX12,3.794542381,3.562499718,4.69902098,4.125119568,2.803836114,3.091647837,3.517316013,4.089928894,3.415454565,3.869498282,2.898768267,4.363728392,3.108521884,4.72878794,4.434718879,4.052008256,4.691595247,3.02868985,4.341666217,4.270569505,3.590584068,3.737254649,4.332484745,4.24674432,3.94303106,4.278668499,4.520279555,3.497641018,4.41415181,4.615879618 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+TEX19,4.232997806,4.855887668,4.261469167,4.435349955,4.37858777,3.815009332,4.51561517,4.285827163,5.730041267,5.053809181,4.936150994,4.329434593,4.355296001,4.854881577,4.870740042,4.620674803,4.783528843,4.610320522,4.53000023,4.095388596,4.507539815,4.46881957,5.570011524,4.964568989,4.245513567,4.276499029,4.651781194,4.515944457,4.843314714,4.781308642 +TEX2,5.474769392,6.308293618,5.785520836,6.006694658,6.158791775,6.148566569,5.93632602,6.09259604,4.792717951,4.536670777,6.468444986,4.970893412,5.359836099,5.258281404,5.05644313,5.637490005,5.045342357,5.526689954,5.321606941,6.192396147,5.156815387,5.860460443,4.841199486,5.470789256,5.503907372,5.753673378,5.518767578,5.154535304,4.944393341,5.182609489 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+TEX26,2.324418897,2.476544895,3.05489772,2.3728726,2.039024517,2.253508613,2.195063732,2.74923034,3.646866931,2.752337483,2.462837146,3.331337777,3.763875739,2.180683832,2.182422658,3.051068697,2.56337953,2.418019843,3.194406977,2.599921903,2.072960391,3.27170392,3.457884302,2.48253733,2.150342133,2.749368153,3.289120849,3.06047396,3.412145934,3.541455274 +TEX26-AS1,3.636302236,3.347311128,4.096199607,4.212604939,3.245438161,3.754290923,3.466612436,3.595130053,3.755854977,4.455395279,3.964557104,3.758577284,5.32258588,4.54316855,4.383143169,3.24896087,3.870251045,3.30980596,3.593382559,3.543235929,3.554029402,3.701875006,3.685250649,4.276526965,3.26745731,3.144254511,4.160081621,3.36327163,4.307581154,3.814919979 +TEX261,5.607458475,5.353452081,4.033602016,4.384446908,5.671665122,6.035059229,5.475203844,5.664340973,4.216980827,4.533431484,4.090095376,3.598380836,4.249858277,4.312058719,4.149364729,5.755872868,4.745877941,5.141747855,5.088689709,5.46097152,4.644663781,5.447068167,4.97767044,5.174963828,5.527890446,5.523039255,3.709895686,4.274359026,2.6005538,5.218790645 +TEX264,4.077008364,3.594371752,4.124923693,4.192063905,3.856592286,4.235586101,3.987353822,4.426134219,3.273001589,4.660092757,3.258946866,5.190886206,4.124923693,3.982484018,4.190801217,4.223026644,4.277287092,3.886062466,3.238865897,4.609441318,4.396864395,4.343743859,3.333227051,3.870314786,3.631114302,4.241721774,4.528562247,4.124923693,4.483078412,4.851812185 +TEX28,4.871254897,4.933638666,4.945079065,4.593225888,5.096823052,5.159002805,4.758949563,5.170997573,4.22907169,5.507132595,5.259626875,5.066699782,4.898318822,5.415995176,5.51951591,5.496160786,5.085255796,5.524549003,5.490834236,5.761064249,5.364635889,5.046224268,5.270388518,5.204953488,5.107180567,5.199796602,6.011634271,5.518826438,5.722816818,4.778375468 +TEX29,5.000911551,4.5354538,4.470926625,3.500707317,3.6509227,3.909227186,4.573911201,4.086177859,4.724091791,3.376106477,3.946252353,3.831262631,4.430318734,3.014610569,4.597962816,3.494244232,5.081587078,4.122947766,4.503251635,4.324181042,4.494424225,3.972159922,3.253471381,4.610862922,4.584937822,4.476405148,3.921794607,4.210507463,4.396562304,4.815584046 +TEX30,4.398336752,3.933903059,4.121804826,4.676732436,3.587111,4.256520333,4.676917986,4.889966901,4.787207794,4.785403425,3.67057741,4.45327927,4.935348463,4.578163167,4.328209932,5.001819486,4.827327575,3.643268351,4.672742511,4.527155105,4.072024958,3.933101095,4.941629998,4.46041724,4.357682955,3.93121498,4.216354557,5.150498191,5.139618677,5.106969726 +TEX35,3.593472406,3.524846689,4.808800413,4.717990295,3.327402084,3.628870918,4.347484012,4.275512092,3.532969933,4.333506158,4.13989402,5.050427387,4.373179389,4.03679118,4.681060276,4.188318077,5.104545416,3.487004793,3.513921607,4.972062169,4.098907382,4.603172538,4.722669489,4.012143177,5.034206239,4.632375178,4.732190523,4.034360884,4.289744468,3.873019501 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+TEX38,4.496266923,4.537310944,4.391887612,4.403653201,4.518480528,4.518480528,4.420032561,4.691552309,4.646165781,5.17189649,3.818412622,4.185528381,4.016372227,4.997998521,4.779721134,3.632988841,4.601938816,4.874578952,4.119918144,4.304711259,3.974703724,4.262975834,4.365537451,5.274516409,4.379494889,4.044444341,5.147764718,4.835498023,3.35413376,4.333738963 +TEX41,3.034679139,3.429994906,3.503542147,3.411920268,2.957578465,3.059663316,3.366178972,3.328134702,3.809268286,3.119610005,3.328134702,3.831820119,3.769495825,3.453435947,3.719922897,3.363679031,3.281090524,2.727413176,2.81270075,3.392464734,3.326398359,3.477218542,3.70355102,3.260326162,3.141027166,3.04852264,3.481213118,3.031042996,3.08077873,3.535127035 +TEX44,2.837571463,3.103865069,3.65487678,3.477521974,3.670489577,3.441105715,3.511506159,3.517090889,5.142989637,4.152338057,4.170632212,4.066604056,4.053320405,4.637161951,4.960159258,4.013309743,3.94445015,4.239933122,4.474635475,3.834152402,4.13513536,3.287355766,4.384204695,3.551456933,3.25514801,3.831811815,4.542894327,3.958443292,3.503412567,3.494932067 +TEX46,3.858861456,3.488744535,3.044074695,3.062118154,3.928496025,3.383941966,3.34281379,2.966534118,3.88522823,4.804292438,4.304573516,4.494273022,4.504419033,3.965915567,4.526794777,4.615399508,3.272288147,2.099676104,4.130067395,4.091651813,3.489261892,4.298269361,4.207826668,3.164968243,3.858513106,3.301039381,3.808763653,3.731264232,3.733504867,4.700726324 +TEX47,4.601081665,3.806059017,3.295957565,4.171865948,3.21500902,2.95089971,3.929756316,4.133469005,3.16966198,5.335288968,3.622354681,3.179510793,3.434236741,3.77351667,4.575940282,3.604265293,3.291478039,2.921497551,4.30000223,3.889312768,4.021193561,2.907289279,3.913099108,2.960538592,3.599753593,2.958093116,3.98762326,4.377094096,3.248017443,4.585779424 +TEX48,3.911926036,5.404706854,5.749582838,5.255919922,4.811632469,3.89087826,5.051812877,5.181677288,6.585272282,5.687210502,5.341199049,4.90923239,5.31005061,5.646583886,4.754122472,5.348381075,4.711870425,3.709490421,4.193298044,5.03020067,4.395632367,5.098800751,5.389032345,5.00242051,5.670451758,4.386773207,5.512613039,4.674818043,4.793965939,5.007699616 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+CASP1,5.54,5.74,4.5,5.42,5.94,4.44,6.29,2.82,5.07,5.77,5.88,3.74,5.05,4.5,4.18,5.51,3.0,4.41,4.66,5.1,4.67,4.74,3.52,4.87,5.21,4.4,4.12,4.8,4.26,5.08,4.58,4.69,4.65,3.4,5.59,5.39,5.37,6.7,5.43,4.51,4.67,6.76,4.62,4.29,4.89,5.39,4.45,4.78,3.94,4.51,3.21,3.42,4.62,4.43,3.88,4.22,3.94,4.68,5.56,5.38,5.56,5.3,5.93,6.38,3.46,4.87,4.0,5.01,3.67,3.32,4.85,4.78,4.36,4.63,4.46,3.91,4.23,4.48,4.76,4.77,5.4,5.26,4.83,5.69,4.48,4.8,4.91,5.07,4.46,5.32,4.38,4.87,4.05,5.07,5.4,4.15,4.58,5.7,4.43,4.61,4.37,4.76,5.32,5.59,4.96,4.62,4.3,5.35,4.44,5.69,5.21,5.7,4.75,5.55,4.73,5.39,4.97,4.79,5.15,5.53,4.42,4.15,3.8,4.91,4.62,5.3,4.4,5.48,4.46,4.33,4.89,5.19,5.11,4.63,5.21,4.89,4.65,5.67,4.52,5.25,4.22,4.19,3.6,4.98,4.32,4.41,4.59,4.72,4.49,5.58,5.1,5.25,4.75,5.29,5.31,5.65,3.78,5.45,4.48,5.61,4.49,5.16,5.13,5.13,4.95,5.4,4.81,5.4,5.06,4.68,6.19,4.89,4.6,4.57,4.96,4.7,5.1,4.91,4.72,4.51,4.41 +CASP10,3.64,3.73,3.14,4.4,2.83,4.18,3.95,4.24,3.44,2.71,3.33,4.24,4.86,5.16,3.95,4.0,4.41,4.66,4.66,3.68,3.65,5.23,4.95,3.93,4.52,4.2,4.63,4.39,3.84,3.69,4.13,4.04,4.58,4.54,4.11,3.82,4.02,3.56,3.47,4.28,4.3,4.3,3.73,4.29,3.99,4.39,5.14,4.36,4.84,4.98,3.73,5.01,3.44,4.46,4.78,4.56,3.94,4.05,3.8,3.63,3.74,3.84,2.7,3.71,3.46,3.73,4.1,2.83,4.16,4.59,3.83,4.02,4.25,4.1,4.24,4.55,4.14,4.11,4.19,4.71,4.58,4.29,5.06,4.7,4.64,4.72,5.12,5.29,5.02,4.97,5.21,5.47,5.5,5.69,4.15,4.3,5.46,5.1,4.94,5.67,5.37,4.32,4.65,4.46,5.62,5.33,5.52,5.14,5.12,4.63,4.77,5.44,5.29,4.1,4.81,4.36,5.08,5.09,4.36,5.09,5.1,5.19,4.11,5.06,5.44,5.25,5.79,5.04,5.24,4.78,5.11,4.11,5.15,5.4,5.01,4.84,5.43,4.89,6.03,4.9,5.38,6.2,5.59,5.75,5.79,6.14,6.14,5.14,5.25,4.63,4.98,4.86,4.93,4.95,5.18,4.45,5.64,4.12,4.97,5.34,4.62,4.5,4.94,4.91,4.68,4.98,4.6,4.63,4.41,4.57,3.52,4.99,5.24,5.17,4.93,4.95,5.03,5.48,5.39,5.13,5.17 +CASP2,5.95,6.29,6.19,6.36,7.1,6.58,6.72,5.88,5.22,6.19,5.88,6.54,7.39,6.41,7.92,5.97,7.33,5.45,6.63,4.71,5.62,6.5,7.66,6.45,5.83,6.44,6.92,6.48,5.88,7.09,6.6,6.36,5.42,7.39,6.28,6.58,6.54,7.02,6.91,6.31,6.42,4.88,6.35,5.82,6.24,7.59,7.17,6.91,7.08,6.57,6.84,7.45,7.02,7.13,6.71,6.25,5.66,6.22,6.04,6.25,6.21,6.79,6.44,6.25,6.85,6.16,6.25,6.08,6.48,6.84,6.29,6.26,6.28,6.49,6.35,6.58,6.58,6.3,6.22,6.27,7.26,6.12,6.72,6.37,6.71,6.32,6.3,6.81,6.32,6.37,6.64,5.81,6.59,6.19,6.74,6.59,6.92,5.94,7.63,6.66,6.16,6.4,6.99,6.63,7.33,6.99,6.53,6.45,5.03,7.12,6.76,7.13,7.31,6.7,6.57,6.37,6.3,6.13,5.9,6.64,5.45,6.35,5.85,7.65,7.0,6.39,6.58,6.59,6.8,6.86,6.44,6.63,6.53,6.3,6.85,6.59,7.08,7.13,6.34,6.09,7.06,7.66,7.24,7.29,6.88,6.26,7.36,7.06,7.6,6.17,6.43,5.95,6.12,6.59,6.16,5.94,7.48,6.2,5.83,6.27,6.31,6.55,6.44,6.6,5.93,6.78,7.2,6.69,6.55,6.45,5.99,6.55,6.28,6.35,6.37,6.45,6.03,6.33,6.75,6.5,6.24 +CASP3,5.02,4.92,5.53,4.4,4.77,4.98,5.84,4.24,4.4,5.14,4.84,5.04,4.18,4.71,4.68,4.58,3.36,4.29,3.68,4.19,5.51,4.74,3.99,4.87,4.94,4.85,3.51,5.04,4.35,4.53,4.2,3.82,4.24,3.56,4.28,5.32,4.13,5.68,4.79,4.51,4.55,4.59,4.42,4.74,4.7,5.25,4.34,4.28,3.09,4.23,4.54,4.08,5.53,4.8,4.35,5.04,4.43,4.63,5.04,4.45,4.78,4.96,5.37,5.06,5.31,3.73,4.64,4.67,3.46,3.24,4.7,5.25,4.54,4.49,4.79,4.32,4.21,4.03,4.52,5.43,5.95,5.1,5.21,5.24,5.8,5.32,4.58,4.8,4.81,5.05,4.48,4.91,4.66,5.28,5.67,4.78,4.75,5.04,4.09,4.61,5.09,5.04,5.37,4.96,4.4,4.82,4.7,5.27,5.26,4.77,4.77,4.62,5.4,5.55,4.85,4.75,5.02,4.55,5.29,5.45,4.18,4.45,4.79,4.55,5.2,5.04,5.0,5.57,5.93,4.06,4.95,4.97,5.21,5.16,4.6,4.8,4.72,4.16,5.26,5.71,5.16,4.02,4.21,5.55,4.19,4.7,5.38,5.11,4.61,5.25,5.58,4.98,5.08,5.24,4.75,5.09,4.89,5.2,5.25,5.28,4.63,4.45,5.36,4.56,4.95,4.08,4.81,5.32,4.41,3.87,5.32,5.39,5.47,5.36,5.19,5.24,4.65,5.2,4.76,4.94,5.28 +CASP8,3.89,2.52,3.14,3.94,3.5,3.6,3.74,3.52,3.97,3.39,4.2,3.99,3.16,3.94,2.59,4.37,3.36,4.53,2.55,4.43,3.39,4.3,4.07,3.93,4.28,3.0,3.41,3.92,3.76,3.4,3.67,3.43,4.24,3.33,3.92,3.56,3.15,3.26,3.47,4.28,3.46,4.88,3.86,3.14,3.84,2.62,3.1,3.81,4.09,4.11,2.83,5.01,3.72,3.55,3.34,3.74,4.18,3.92,3.75,3.22,3.52,3.15,3.16,2.41,4.42,3.39,4.27,1.59,3.24,3.15,3.51,4.1,3.95,3.18,3.83,3.68,3.63,3.85,3.7,5.49,4.77,5.17,4.73,4.9,4.87,5.11,4.98,4.8,5.49,5.21,4.75,5.06,4.66,4.73,5.84,5.54,4.81,4.58,4.43,5.08,5.37,5.72,4.5,4.4,4.93,4.52,4.93,4.98,6.37,4.99,5.62,4.5,4.11,4.61,4.7,4.96,5.17,5.2,5.05,4.58,5.1,5.84,5.25,4.55,4.89,4.89,5.04,5.19,5.04,5.18,5.4,4.84,5.37,5.16,4.62,5.09,5.25,4.64,5.74,5.25,4.09,4.19,5.02,4.98,5.16,4.97,5.0,5.28,4.77,5.18,5.1,3.14,4.68,4.55,5.23,4.94,4.5,5.56,5.71,4.6,4.96,4.74,5.01,4.52,5.79,4.78,4.12,5.02,4.3,4.21,4.45,4.86,5.04,4.8,4.69,4.81,4.72,4.55,4.47,4.51,5.12 +CCL11,4.64,4.4,4.21,6.05,6.18,5.24,3.95,2.82,3.67,5.08,4.35,3.74,6.81,5.44,5.39,4.86,5.53,4.94,4.66,3.84,5.01,5.64,6.45,4.94,4.04,4.2,7.92,4.39,4.48,5.56,5.17,5.3,5.19,6.68,6.67,5.32,5.2,5.37,5.39,4.07,2.44,3.6,5.5,5.38,5.85,6.7,5.95,6.14,4.26,5.9,6.62,6.98,5.17,5.67,6.02,4.34,6.14,4.06,4.73,4.84,4.45,6.14,2.7,6.03,3.77,5.28,6.05,4.02,6.05,6.26,4.77,4.18,4.92,5.39,4.91,5.53,5.7,4.87,4.85,5.78,5.89,5.61,5.89,6.5,5.05,5.11,6.57,5.78,5.49,5.43,6.35,6.16,6.94,6.69,4.15,6.88,6.06,6.01,6.97,6.62,5.85,5.14,5.76,6.58,7.22,7.19,4.83,6.13,5.0,5.98,6.82,5.74,6.49,6.7,6.3,5.58,5.51,5.4,6.24,6.03,6.05,7.12,8.05,6.85,5.93,5.73,5.63,5.73,4.81,6.01,5.47,6.12,5.91,5.76,6.24,6.2,5.92,6.1,5.48,5.77,5.09,7.55,5.96,6.99,6.57,6.92,5.38,6.08,6.15,5.7,5.43,6.55,5.72,5.63,6.19,6.29,7.47,5.42,5.71,5.39,5.88,6.42,6.06,6.44,6.25,6.37,6.38,5.63,6.64,6.31,6.23,5.65,5.56,6.04,5.8,5.65,5.56,5.84,6.62,6.28,5.72 +CCL13,3.64,3.92,3.7,3.44,3.5,2.62,4.13,3.52,3.67,4.09,3.44,3.99,3.89,3.94,3.24,3.66,3.36,3.53,3.93,3.38,4.07,3.89,3.99,2.46,4.18,3.4,3.25,3.79,3.47,2.81,3.67,3.43,2.92,3.4,3.4,4.56,3.15,2.32,2.04,3.52,4.02,3.22,3.41,3.14,3.08,4.68,3.45,3.68,3.94,2.83,3.6,3.6,3.14,3.64,3.28,3.74,3.7,3.14,3.24,2.72,2.93,4.21,3.5,4.0,5.1,4.06,4.0,2.83,3.48,3.15,3.51,2.64,3.81,3.44,3.68,3.27,3.58,3.21,3.63,3.68,2.61,3.31,4.06,4.48,4.04,3.65,3.87,4.98,4.08,4.0,4.29,4.73,4.05,3.79,4.15,4.78,4.37,3.5,3.83,4.08,4.18,4.9,4.39,3.88,4.8,4.06,3.75,3.48,4.21,3.23,5.21,3.13,3.96,4.61,3.61,3.52,4.04,4.07,4.11,3.76,4.56,3.91,3.8,4.12,3.58,3.72,4.03,3.79,4.03,4.33,3.5,2.61,3.68,4.24,3.99,4.29,4.03,3.61,4.31,4.1,3.98,4.02,4.74,4.0,3.65,4.16,3.78,3.88,4.12,3.51,3.88,4.3,4.14,4.28,4.36,3.52,4.56,3.2,4.48,3.8,3.69,3.88,4.06,4.3,4.52,3.74,4.38,3.46,4.03,4.43,4.06,2.65,3.51,3.52,4.35,3.39,3.08,4.0,3.96,3.52,3.78 +CCL15,3.42,3.73,2.45,4.58,5.01,4.25,3.18,4.24,3.22,3.6,3.12,3.18,4.47,4.1,4.68,3.66,4.55,3.53,6.22,1.65,4.44,4.23,5.38,3.09,3.61,3.96,5.93,3.92,2.97,3.94,4.28,5.23,3.47,6.5,4.55,2.6,4.6,3.82,4.31,4.07,3.46,2.76,3.73,4.61,4.55,3.28,2.98,4.43,4.42,3.68,6.42,3.6,4.23,5.01,4.71,3.12,3.94,2.73,3.64,4.61,3.6,4.39,4.02,3.19,4.9,3.39,4.55,4.5,5.28,5.65,3.96,3.68,4.12,5.21,3.87,4.93,4.48,3.68,3.96,5.19,5.76,4.72,4.83,4.34,5.24,4.97,5.99,5.55,5.88,5.39,6.04,5.14,5.24,5.28,5.84,5.37,5.03,5.24,6.16,5.62,5.52,4.9,3.91,3.76,5.8,6.9,5.29,5.56,4.13,4.0,4.77,5.26,5.18,5.73,5.02,4.73,5.02,5.68,5.25,4.96,5.66,4.92,4.11,5.88,5.88,5.46,5.97,5.41,5.55,5.34,4.95,4.63,5.4,5.92,5.52,5.64,4.87,5.39,5.09,3.78,5.79,6.52,6.13,5.33,5.46,5.66,5.66,5.39,5.94,4.41,5.9,5.9,5.08,5.94,5.31,4.71,5.89,5.84,6.39,4.11,5.36,5.1,4.94,5.01,5.61,5.3,5.57,4.06,5.31,5.52,4.2,5.33,4.6,5.29,5.51,5.39,5.48,5.72,5.77,5.73,5.11 +CCL16,3.18,2.99,2.8,3.44,3.19,3.4,2.8,3.72,2.92,3.86,3.53,4.24,2.41,4.25,1.76,3.51,4.05,3.29,3.4,2.53,3.89,4.07,4.32,3.09,3.61,2.39,4.74,3.12,2.97,2.37,3.47,3.82,3.96,4.36,3.85,3.56,2.56,4.07,3.72,3.12,3.18,3.22,3.18,3.79,3.6,3.42,4.08,3.22,3.74,3.83,3.91,4.27,3.44,3.64,4.33,3.45,4.87,2.77,3.13,3.95,3.28,4.35,3.16,3.19,2.45,4.4,4.8,3.41,4.44,4.37,4.29,3.68,4.04,3.77,3.98,4.32,3.92,3.8,3.68,4.88,4.23,4.62,4.54,4.62,4.45,4.58,4.64,4.7,3.98,4.58,4.29,4.22,5.24,5.39,5.07,5.02,4.86,4.02,5.11,4.89,4.96,5.14,4.08,4.67,5.25,5.08,4.45,4.57,5.01,4.3,4.77,5.0,4.11,3.77,4.29,4.24,4.51,4.83,3.19,3.89,4.42,4.84,4.11,5.41,4.39,3.83,4.48,3.7,4.33,4.89,4.37,4.03,4.29,4.55,4.75,5.21,4.15,4.89,4.66,4.17,5.25,5.43,5.32,4.77,4.78,4.78,4.8,5.49,4.36,4.36,4.94,4.86,3.55,4.01,4.58,4.22,5.14,4.51,4.97,4.11,4.5,4.7,4.68,5.18,4.33,4.55,4.81,4.06,4.54,4.9,3.52,4.64,4.78,5.12,5.11,4.73,4.15,4.71,4.89,4.81,4.86 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+ITGA6,4.54,5.98,4.5,2.76,4.84,4.85,5.77,4.24,4.28,4.97,5.37,3.99,4.07,3.71,3.69,3.59,3.87,4.01,3.93,3.51,4.67,4.3,4.63,3.93,5.58,3.0,3.41,4.25,3.94,4.26,4.85,3.79,3.62,3.13,4.5,6.03,3.45,4.07,5.13,4.51,4.77,3.95,4.29,5.01,4.65,5.75,4.73,3.94,3.09,4.11,4.67,3.24,4.0,4.72,4.14,4.56,3.94,4.76,5.87,4.49,5.71,5.12,4.76,4.0,4.42,4.4,4.0,5.55,3.89,3.97,5.06,4.65,5.3,4.3,5.52,4.21,4.23,4.94,5.52,5.96,6.47,6.22,5.35,5.38,6.25,5.78,5.88,5.07,6.52,6.02,5.62,6.12,4.66,5.6,6.35,4.56,5.17,5.14,4.59,4.8,6.04,5.28,4.8,4.7,4.35,5.17,5.58,5.97,6.08,5.28,5.33,5.7,5.4,5.55,5.79,5.34,6.09,6.06,6.28,5.76,5.34,4.09,4.79,5.04,5.44,6.16,5.86,6.24,5.8,5.78,5.85,5.98,6.22,5.86,5.77,6.04,6.22,5.76,5.48,6.01,4.59,4.3,5.25,4.23,6.0,5.26,6.14,5.02,5.13,5.25,6.55,5.71,5.85,6.35,5.95,5.56,4.37,6.39,6.47,5.14,5.87,5.35,6.66,5.23,6.46,4.86,4.38,5.78,4.82,4.9,5.47,6.56,6.66,6.74,6.4,6.6,6.9,6.06,5.47,5.88,6.94 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diff --git a/p3/preprocess/Melanoma/clinical_data/GSE144296.csv b/p3/preprocess/Melanoma/clinical_data/GSE144296.csv new file mode 100644 index 0000000000000000000000000000000000000000..97e4bfaf9e7894fe6686ba8931f0a01711ad648f --- /dev/null +++ b/p3/preprocess/Melanoma/clinical_data/GSE144296.csv @@ -0,0 +1,2 @@ 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diff --git a/p3/preprocess/Melanoma/clinical_data/GSE146264.csv b/p3/preprocess/Melanoma/clinical_data/GSE146264.csv new file mode 100644 index 0000000000000000000000000000000000000000..6d309c1060a6c17dfd4d1aa25a6b4c3594048c5c --- /dev/null +++ b/p3/preprocess/Melanoma/clinical_data/GSE146264.csv @@ -0,0 +1,2 @@ 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diff --git a/p3/preprocess/Melanoma/clinical_data/GSE202806.csv b/p3/preprocess/Melanoma/clinical_data/GSE202806.csv new file mode 100644 index 0000000000000000000000000000000000000000..156941c77d41cde85a1f43ecccda566bf7741660 --- /dev/null +++ b/p3/preprocess/Melanoma/clinical_data/GSE202806.csv @@ -0,0 +1,2 @@ +,GSM6133552,GSM6133553,GSM6133554,GSM6133555,GSM6133556,GSM6133557,GSM6133558,GSM6133559,GSM6133560,GSM6133561,GSM6133562,GSM6133563,GSM6133564,GSM6133565,GSM6133566,GSM6133567,GSM6133568,GSM6133569,GSM6133570,GSM6133571,GSM6133572,GSM6133573,GSM6133574,GSM6133575,GSM6133576,GSM6133577,GSM6133578,GSM6133579,GSM6133580,GSM6133581,GSM6133582,GSM6133583,GSM6133584,GSM6133585,GSM6133586,GSM6133587,GSM6133588,GSM6133589,GSM6133590,GSM6133591,GSM6133592,GSM6133593,GSM6133594,GSM6133595,GSM6133596,GSM6133597,GSM6133598,GSM6133599,GSM6133600,GSM6133601,GSM6133602,GSM6133603 +Melanoma,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0 diff --git a/p3/preprocess/Melanoma/clinical_data/GSE215868.csv b/p3/preprocess/Melanoma/clinical_data/GSE215868.csv new file mode 100644 index 0000000000000000000000000000000000000000..1180994c0731d854c2b15c8d34af81033a1206c2 --- /dev/null +++ b/p3/preprocess/Melanoma/clinical_data/GSE215868.csv @@ -0,0 +1,3 @@ +,GSM6644725,GSM6644726,GSM6644727,GSM6644728,GSM6644729,GSM6644730,GSM6644731,GSM6644732,GSM6644733,GSM6644734,GSM6644735,GSM6644736,GSM6644737,GSM6644738,GSM6644739,GSM6644740,GSM6644741,GSM6644742,GSM6644743,GSM6644744,GSM6644745,GSM6644746,GSM6644747,GSM6644748,GSM6644749,GSM6644750,GSM6644751,GSM6644752,GSM6644753,GSM6644754,GSM6644755,GSM6644756,GSM6644757,GSM6644758,GSM6644759,GSM6644760,GSM6644761,GSM6644762,GSM6644763,GSM6644764,GSM6644765,GSM6644766,GSM6644767,GSM6644768,GSM6644769,GSM6644770,GSM6644771,GSM6644772,GSM6644773,GSM6644774,GSM6644775,GSM6644776,GSM6644777,GSM6644778,GSM6644779,GSM6644780,GSM6644781,GSM6644782,GSM6644783,GSM6644784,GSM6644785,GSM6644786,GSM6644787,GSM6644788,GSM6644789,GSM6644790,GSM6644791,GSM6644792,GSM6644793,GSM6644794,GSM6644795,GSM6644796,GSM6644797,GSM6644798,GSM6644799,GSM6644800,GSM6644801,GSM6644802,GSM6644803,GSM6644804,GSM6644805,GSM6644806,GSM6644807,GSM6644808,GSM6644809,GSM6644810,GSM6644811,GSM6644812,GSM6644813,GSM6644814,GSM6644815,GSM6644816,GSM6644817,GSM6644818,GSM6644819,GSM6644820,GSM6644821,GSM6644822,GSM6644823,GSM6644824,GSM6644825,GSM6644826,GSM6644827,GSM6644828,GSM6644829 +Melanoma,,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,,1.0,0.0,1.0,0.0,1.0,0.0,0.0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,,0.0,,0.0,0.0,,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,,1.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,,,0.0,,0.0,0.0,,0.0,,1.0,1.0,0.0,0.0,0.0,0.0,1.0,,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,1.0,0.0,0.0, +Age,71.0,59.0,63.0,44.0,56.0,58.0,74.0,51.0,76.0,63.0,56.0,16.0,72.0,73.0,34.0,64.0,59.0,68.0,36.0,58.0,52.0,54.0,85.0,82.0,46.0,76.0,69.0,40.0,69.0,75.0,86.0,50.0,77.0,54.0,88.0,38.0,61.0,60.0,78.0,59.0,36.0,71.0,88.0,73.0,67.0,57.0,68.0,30.0,40.0,56.0,79.0,62.0,64.0,76.0,83.0,82.0,62.0,59.0,60.0,39.0,55.0,74.0,36.0,71.0,67.0,63.0,51.0,69.0,65.0,44.0,61.0,70.0,76.0,85.0,31.0,48.0,78.0,52.0,82.0,63.0,62.0,44.0,54.0,85.0,63.0,78.0,85.0,70.0,52.0,87.0,37.0,65.0,88.0,82.0,61.0,32.0,78.0,78.0,57.0,49.0,79.0,78.0,74.0,75.0,68.0 diff --git a/p3/preprocess/Melanoma/clinical_data/GSE244984.csv b/p3/preprocess/Melanoma/clinical_data/GSE244984.csv new file mode 100644 index 0000000000000000000000000000000000000000..371f132e029f68fd03a446edfe5afb849cbd3aa2 --- /dev/null +++ b/p3/preprocess/Melanoma/clinical_data/GSE244984.csv @@ -0,0 +1,2 @@ +,GSM8140965,GSM8140966,GSM8140967,GSM8140968,GSM8140969,GSM8140970,GSM8140971,GSM8140972,GSM8140973,GSM8140974,GSM8140975,GSM8140976,GSM8140977,GSM8140978,GSM8140979,GSM8140980,GSM8140981,GSM8140982,GSM8140983,GSM8140984,GSM8140985,GSM8140986,GSM8140987,GSM8140988,GSM8140989,GSM8140990,GSM8140991,GSM8140992,GSM8140993,GSM8140994,GSM8140995,GSM8140996,GSM8140997 +Melanoma,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0 diff --git a/p3/preprocess/Melanoma/clinical_data/GSE261347.csv b/p3/preprocess/Melanoma/clinical_data/GSE261347.csv new file mode 100644 index 0000000000000000000000000000000000000000..371f132e029f68fd03a446edfe5afb849cbd3aa2 --- /dev/null +++ b/p3/preprocess/Melanoma/clinical_data/GSE261347.csv @@ -0,0 +1,2 @@ +,GSM8140965,GSM8140966,GSM8140967,GSM8140968,GSM8140969,GSM8140970,GSM8140971,GSM8140972,GSM8140973,GSM8140974,GSM8140975,GSM8140976,GSM8140977,GSM8140978,GSM8140979,GSM8140980,GSM8140981,GSM8140982,GSM8140983,GSM8140984,GSM8140985,GSM8140986,GSM8140987,GSM8140988,GSM8140989,GSM8140990,GSM8140991,GSM8140992,GSM8140993,GSM8140994,GSM8140995,GSM8140996,GSM8140997 +Melanoma,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0 diff --git a/p3/preprocess/Melanoma/code/GSE144296.py b/p3/preprocess/Melanoma/code/GSE144296.py new file mode 100644 index 0000000000000000000000000000000000000000..ed2158b81a4a048a8401811ae8d23834d2f61707 --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE144296.py @@ -0,0 +1,245 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE144296" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE144296" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE144296.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE144296.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE144296.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on background info mentioning mRNA sequencing and gene expression analysis +is_gene_available = True + +# 2.1 Data Availability +# Trait (melanoma vs non-melanoma) can be inferred from cell type field (row 1) +trait_row = 1 +# Age not available in data +age_row = None +# Gender not available in data +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + """Convert cell type to binary melanoma indicator""" + if not isinstance(x, str): + return None + x = x.lower().split(': ')[-1] + if 'melanoma' in x: + return 1 + elif 'colorectal' in x: + return 0 + return None + +def convert_age(x): + """Placeholder for age conversion""" + return None + +def convert_gender(x): + """Placeholder for gender conversion""" + return None + +# 3. Save metadata for initial filtering +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + preview_df(selected_clinical) + + # Save clinical features + selected_clinical.to_csv(out_clinical_data_file) +# Extract genetic data matrix with case-insensitive marker +genetic_data = get_genetic_data(matrix_file_path, marker="!series_matrix_table_begin".lower()) + +# Verify data was loaded +if len(genetic_data.index) == 0: + # Try alternative marker format + genetic_data = get_genetic_data(matrix_file_path, marker="!Series_Matrix_Table_Begin") + +if len(genetic_data.index) == 0: + print("Warning: No data was extracted from the matrix file. Please check the matrix file formatting.") + is_gene_available = False +else: + print("First 20 row IDs:") + print(list(genetic_data.index)[:20]) + is_gene_available = True + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +if is_gene_available: + genetic_data.to_csv(out_gene_data_file) +# Examine file content before filtering +with gzip.open(soft_file_path, 'rt') as f: + print("\nSample of unfiltered SOFT file content (first 20 lines):") + for i, line in enumerate(f): + if i < 20: # Print more lines to better understand the structure + print(line.strip()) + elif i == 20: + print("...") + break + +# Try reading the matrix file for gene annotations since the SOFT file seems to lack them +with gzip.open(matrix_file_path, 'rt') as f: + print("\nSample of matrix file content (first 20 lines):") + for i, line in enumerate(f): + if i < 20: + print(line.strip()) + elif i == 20: + print("...") + break + +# Since we can see the file content now, update the gene metadata extraction +probe_info_found = False +with gzip.open(matrix_file_path, 'rt') as f: + lines = [] + for line in f: + if line.startswith('!Platform_organism'): + probe_info_found = True + lines.append(line) + elif probe_info_found and line.startswith('!'): + lines.append(line) + elif probe_info_found and not any(line.startswith(p) for p in ['!', '#', '^']): + break + gene_metadata_text = '\n'.join(lines) + +print("\nExtracted probe/gene information:") +print(gene_metadata_text) +# Try reading gene expression data using the library function +genetic_data = get_genetic_data(matrix_file_path) + +# Convert index to string type +genetic_data.index = genetic_data.index.astype(str) + +# Print sample identifiers for verification +print("\nSample identifiers from genetic data:") +print(list(genetic_data.index)[:5]) + +# Extract gene mapping info +try: + with gzip.open(matrix_file_path, 'rt') as f: + for line in f: + if '!series_matrix_table_begin' in line.lower(): + # Found start of expression data + break + if line.startswith('!Sample_platform_id'): + # Save the platform ID if we find it + platform_line = line.strip() + + # For RNA-seq data, create a 1:1 mapping using the original gene identifiers + ids = genetic_data.index.tolist() + annotation_df = pd.DataFrame({ + 'ID': ids, + 'Gene': ids # Use same IDs as gene symbols for now + }) + + print("\nSample rows from annotation mapping:") + print(annotation_df.head()) + + # Apply gene mapping using library function + gene_data = apply_gene_mapping(genetic_data, annotation_df) + + # Convert gene indices to string before normalization + gene_data.index = gene_data.index.astype(str) + + # Normalize gene symbols + gene_data = normalize_gene_symbols_in_index(gene_data) + + print("\nFinal gene data shape:", gene_data.shape) + print("Sample gene names after normalization:") + print(list(gene_data.index)[:5]) + + # Save the processed gene data + gene_data.to_csv(out_gene_data_file) + +except Exception as e: + print(f"Error during gene mapping: {str(e)}") + # Save genetic data without mapping if error occurs + genetic_data.to_csv(out_gene_data_file) +# Read the entire file first to find the exact line numbers of begin/end markers +with gzip.open(matrix_file_path, 'rt') as f: + lines = f.readlines() + +start_idx = None +end_idx = None +for i, line in enumerate(lines): + if '!series_matrix_table_begin' in line.lower(): + start_idx = i + 1 # Skip the marker line + elif '!series_matrix_table_end' in line.lower(): + end_idx = i + break + +genetic_data = None +if start_idx and end_idx: + # Read only the data section + genetic_data = pd.read_csv(io.StringIO(''.join(lines[start_idx:end_idx])), + sep='\t', index_col=0) + +# Print results +if genetic_data is not None and len(genetic_data) > 0: + print("\nFirst 20 row IDs:") + print(list(genetic_data.index)[:20]) + is_gene_available = True +else: + print("\nWarning: No gene expression data could be extracted") + is_gene_available = False + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +if is_gene_available: + genetic_data.to_csv(out_gene_data_file) \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE146264.py b/p3/preprocess/Melanoma/code/GSE146264.py new file mode 100644 index 0000000000000000000000000000000000000000..c9725ac2c808e560cdb6f5e8b46dfa302ce68e2c --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE146264.py @@ -0,0 +1,238 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE146264" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE146264" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE146264.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE146264.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE146264.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene expression data availability +is_gene_available = True # This is an scRNA-seq dataset for CD8+ T cells + +# 2. Clinical data availability and conversion +trait_row = 1 # subjectid indicates disease status (P for psoriasis patients, C for controls) +age_row = None # Age data not available +gender_row = None # Gender data not available + +def convert_trait(x: str) -> int: + """Convert subject ID to binary trait status + P = patient = 1, C = control = 0""" + if not x or ':' not in x: + return None + val = x.split(':')[1].strip() + if val.startswith('P'): # Patient + return 1 + elif val.startswith('C'): # Control + return 0 + return None + +def convert_age(x: str) -> float: + """Convert age string to float""" + return None # Not used since age_row is None + +def convert_gender(x: str) -> int: + """Convert gender string to binary""" + return None # Not used since gender_row is None + +# 3. Save initial metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Extract clinical features +if trait_row is not None: + selected_clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + preview = preview_df(selected_clinical_df) + print("Preview of processed clinical data:") + print(preview) + + # Save clinical data + selected_clinical_df.to_csv(out_clinical_data_file) +# Try different markers for gene data extraction +markers = ["!series_matrix_table_begin", "!series_matrix_table_begin\t", "!dataset_table_begin"] + +for marker in markers: + genetic_data = get_genetic_data(matrix_file_path, marker=marker) + if not genetic_data.empty: + break + +if genetic_data.empty: + print("Warning: No genetic data was extracted from the matrix file.") + is_gene_available = False +else: + # Print first 20 row IDs to examine data type + print("First 20 row IDs:") + print(list(genetic_data.index)[:20]) + is_gene_available = True + # Only save if data was successfully extracted + genetic_data.to_csv(out_gene_data_file) + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) +# Peek at file structure +with gzip.open(matrix_file_path, 'rt') as f: + print("First 10 lines of matrix file:") + for i, line in enumerate(f): + if i < 10: + print(line.strip()) + else: + break + +# First try reading as tab-delimited without seeking markers +try: + genetic_data = pd.read_csv(matrix_file_path, compression='gzip', sep='\t', comment='!', + low_memory=False) + print("\nLoaded data shape:", genetic_data.shape) + if not genetic_data.empty: + if 'ID_REF' in genetic_data.columns: + genetic_data = genetic_data.rename(columns={'ID_REF': 'ID'}) + genetic_data = genetic_data.set_index(genetic_data.columns[0]) + # Print first 20 row IDs to examine data type + print("\nFirst 20 row IDs:") + print(list(genetic_data.index)[:20]) + genetic_data.to_csv(out_gene_data_file) + is_gene_available = True + else: + print("Warning: No genetic data was extracted from the matrix file.") + is_gene_available = False + +except Exception as e: + print(f"Error extracting genetic data: {str(e)}") + is_gene_available = False + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) +requires_gene_mapping = False +# First peek at SOFT file structure +with gzip.open(soft_file_path, 'rt') as f: + print("First 20 lines of SOFT file:") + # Store lines that don't start with ^, !, or # + data_lines = [] + for i, line in enumerate(f): + if i < 20: + print(line.strip()) + if not any(line.startswith(p) for p in ['^', '!', '#']): + data_lines.append(line) + if len(data_lines) >= 5: # Get first few data lines + break + +# Manual parsing approach since file structure is non-standard +try: + with gzip.open(soft_file_path, 'rt') as f: + data_lines = [] + for line in f: + if not any(line.startswith(p) for p in ['^', '!', '#']): + data_lines.append(line) + + if data_lines: + gene_metadata = pd.read_csv(io.StringIO(''.join(data_lines)), sep='\t', + low_memory=False) + print("\nLoaded data shape:", gene_metadata.shape) + # Preview column names and first few values + preview = preview_df(gene_metadata) + print("\nGene annotation columns and sample values:") + print(preview) + else: + print("Warning: No gene annotation data was found in the SOFT file.") + +except Exception as e: + print(f"Error extracting gene annotation data: {str(e)}") +# Check if we have valid gene expression data +if 'genetic_data' not in locals() or genetic_data.empty: + print("No valid gene expression data available. Skipping data integration.") + # Create minimal DataFrame to indicate failure + minimal_df = pd.DataFrame({'Failed': [1]}) + validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=False, + is_trait_available=True, + is_biased=True, # Set to True to indicate dataset is unusable + df=minimal_df, + note="Failed to extract gene expression data from matrix file." + ) +else: + # 1. Normalize gene symbols and save gene data + normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) + os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) + normalized_gene_data.to_csv(out_gene_data_file) + + # 2. Link clinical and genetic data + clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) + linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data) + + # 3. Handle missing values + linked_data = handle_missing_values(linked_data, trait) + + # 4. Judge bias in features and remove biased ones + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Final validation and save metadata + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome." + ) + + # 6. Save linked data if usable + if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE148319.py b/p3/preprocess/Melanoma/code/GSE148319.py new file mode 100644 index 0000000000000000000000000000000000000000..832db706cf19a9d0c2c518885008383802117e06 --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE148319.py @@ -0,0 +1,154 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE148319" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE148319" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE148319.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE148319.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE148319.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene expression data availability +# Based on background info, this is a study with gene expression data from tumor xenografts +is_gene_available = True + +# 2.1 Data availability +# For trait - can be inferred from cell line info in row 8 +trait_row = 8 + +# Age & gender not recorded in the sample characteristics +age_row = None +gender_row = None + +# 2.2 Data type conversion functions +def convert_trait(value: str) -> int: + """Convert cell line info to binary melanoma status""" + if "melanoma" in value.lower(): + return 1 + elif "oral carcinoma" in value.lower(): + return 0 + return None + +def convert_age(value: str) -> float: + return None + +def convert_gender(value: str) -> int: + return None + +# 3. Save metadata +_ = validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None) + +# 4. Extract clinical features since trait_row is not None +selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + +# Preview the processed clinical data +print(preview_df(selected_clinical_df)) + +# Save clinical data +selected_clinical_df.to_csv(out_clinical_data_file) +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs to examine data type +print("First 20 row IDs:") +print(list(genetic_data.index)[:20]) + +# After examining the IDs and confirming this is gene expression data: +is_gene_available = True + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +genetic_data.to_csv(out_gene_data_file) +# The pattern of identifiers shows these are probe IDs from an Affymetrix microarray platform +# (format: number_at, number_s_at, number_x_at) +# They need to be mapped to human gene symbols for proper analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Extract gene mapping from annotation data +# ID column contains probe IDs that match gene expression data +# Gene Symbol column contains standardized human gene symbols +mapping_df = get_gene_mapping(gene_metadata, "ID", "Gene Symbol") + +# Convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols and save gene data +normalized_gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE148949.py b/p3/preprocess/Melanoma/code/GSE148949.py new file mode 100644 index 0000000000000000000000000000000000000000..5fd751375a2d645f1e915578368c33d6449b2837 --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE148949.py @@ -0,0 +1,144 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE148949" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE148949" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE148949.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE148949.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE148949.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Looking at series title and summary, this appears to be a microarray study of breast cancer models +# with gene expression data from Agilent arrays +is_gene_available = True + +# 2.1 Data Availability +# From sample characteristics, this dataset contains reference samples from various cell lines +# including melanoma (line 6). However it's just a reference pool, not experimental samples +# so no real trait/phenotype data is available +trait_row = None +age_row = None +gender_row = None + +# 3. Save Metadata +# Only has gene expression data but no trait data for analysis +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False) + +# 4. Skip clinical feature extraction since trait_row is None +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs to examine data type +print("First 20 row IDs:") +print(list(genetic_data.index)[:20]) + +# After examining the IDs and confirming this is gene expression data: +is_gene_available = True + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +genetic_data.to_csv(out_gene_data_file) +# Based on my biomedical expertise, looking at the gene identifiers: +# The numeric identifiers (e.g. '41334', '41335' etc.) and '1/2-SBSRNA4' +# appear to be probe IDs or array feature numbers rather than standard human gene symbols +# Gene symbols would typically be in formats like 'BRAF', 'NRAS', 'TP53' +# Therefore this data requires mapping from probe IDs to gene symbols + +requires_gene_mapping = True +# Extract gene annotation data from platform section of SOFT file +gene_metadata = get_gene_annotation(soft_file_path) + +# Check available columns to find probe ID and gene symbol mappings +print("\nGene annotation data shape:", gene_metadata.shape) +print("\nGene annotation columns:") +print(gene_metadata.columns) + +# Preview first few rows to understand data structure +print("\nFirst few rows:") +print(gene_metadata.head()) + +# Look for probe ID patterns in each column +for col in gene_metadata.columns: + print(f"\nSample values from column '{col}':") + sample_vals = gene_metadata[col].head(10).tolist() + print(sample_vals) + +# Based on the output, determine map_config +probe_col = None +gene_col = None + +for col in gene_metadata.columns: + # Compare values to gene expression index + sample_vals = set(gene_metadata[col].astype(str).head(100)) + genetic_ids = set(list(genetic_data.index)[:100]) + overlap = sample_vals & genetic_ids + if len(overlap) > 0: + probe_col = col + break + +# Print mapping column candidates +print("\nMapping columns found:") +print(f"Probe ID column: {probe_col}") +print(f"Gene Symbol column: {gene_col}") +# The index already contains gene symbols (e.g. A1BG, A1CF) as seen in output +gene_data = genetic_data.copy() + +# Normalize gene symbols to ensure consistency +gene_data = normalize_gene_symbols_in_index(gene_data) + +print("\nFirst 10 rows of processed gene expression data:") +print(gene_data.head(10)) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# No clinical data available, so can't perform associative analysis +# But provide gene_data for validation and indicate bias +validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=True, # Can't do association analysis without trait data + df=gene_data, # Provide gene expression data for validation + note="Dataset contains only reference samples from cell lines. No trait data available for analysis." +) + +# Skip saving linked data since dataset is not usable without trait data \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE157738.py b/p3/preprocess/Melanoma/code/GSE157738.py new file mode 100644 index 0000000000000000000000000000000000000000..212ac4fa2837f09345195d8d5a5826de93d7143a --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE157738.py @@ -0,0 +1,354 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE157738" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE157738" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE157738.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE157738.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE157738.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this is gene expression data from Affymetrix Human Gene 2.0 ST Array +is_gene_available = True + +# 2.1 Data Availability +# Trait (clinical outcome) is available in row 4 with multiple values +trait_row = 4 + +# Age and gender data are not available in sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # Extract value after colon, strip whitespace + if not isinstance(x, str): + return None + value = x.split(':')[-1].strip() + # Convert clinical outcomes to binary + # NED (No Evidence of Disease) and PR (Partial Response) are positive outcomes + if value in ['NED1', 'NED2', 'PR']: + return 1 + # PD (Progressive Disease) and SD (Stable Disease) are negative outcomes + elif value in ['PD', 'SD']: + return 0 + return None + +def convert_age(x): + return None # Age data not available + +def convert_gender(x): + return None # Gender data not available + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Clinical Feature Extraction +# Since trait_row is not None, we need to extract clinical features +selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the processed clinical data +preview_result = preview_df(selected_clinical) + +# Save clinical data +selected_clinical.to_csv(out_clinical_data_file) +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs to examine data type +print("First 20 row IDs:") +print(list(genetic_data.index)[:20]) + +# After examining the IDs and confirming this is gene expression data: +is_gene_available = True + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +genetic_data.to_csv(out_gene_data_file) +# These numerical IDs appear to be probe IDs, not standard human gene symbols +# They need to be mapped to their corresponding gene symbols for biological interpretation +requires_gene_mapping = True +# First let's examine a few lines from the SOFT file to identify the correct section +import gzip +with gzip.open(soft_file_path, 'rt') as f: + # Print first 100 lines to see file structure + for i, line in enumerate(f): + if i < 100: # Limit output to first 100 lines + if 'table_begin' in line.lower(): + print(f"Found table marker at line {i}:") + print(line.strip()) + else: + break + +# Extract gene annotation with adjusted prefix filtering +gene_metadata = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin', '!platform_table_end']) + +# Preview to verify we got the annotation data +print("\nGene annotation columns and sample values:") +preview = preview_df(gene_metadata) +print(preview) +# Try loading annotation data with platform-related prefixes +import gzip + +def parse_soft_file(file_path): + probe_to_gene = {} + within_platform = False + with gzip.open(file_path, 'rt') as f: + for line in f: + line = line.strip() + if line.startswith('!Platform_table_begin'): + within_platform = True + # Get header line + header = next(f).strip().split('\t') + id_idx = header.index('ID') + gene_idx = header.index('Gene Assignment') + continue + + if within_platform: + if line.startswith('!Platform_table_end'): + break + + fields = line.split('\t') + if len(fields) > max(id_idx, gene_idx): + probe_id = fields[id_idx] + gene_info = fields[gene_idx] + if gene_info != '---': + # Extract gene symbol from gene assignment string + # Format is typically: gene_id // gene_symbol // gene_name + gene_parts = gene_info.split('//') + if len(gene_parts) > 1: + gene_symbol = gene_parts[1].strip() + probe_to_gene[probe_id] = gene_symbol + + # Convert to DataFrame + mapping_df = pd.DataFrame.from_dict(probe_to_gene.items()) + mapping_df.columns = ['ID', 'Gene'] + return mapping_df + +# Get mapping between probe IDs and gene symbols +mapping_data = parse_soft_file(soft_file_path) + +# Apply gene mapping to convert probe-level data to gene-level expression +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview mapped gene data +print("\nFirst few gene symbols:") +print(list(gene_data.index)[:10]) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# Parse SOFT file to get probe-to-gene mapping +def parse_soft_file(file_path): + probe_to_gene = [] + within_platform = False + + with gzip.open(file_path, 'rt') as f: + # Debug printing + print("First 10 lines of SOFT file:") + for i, line in enumerate(f): + if i < 10: + print(line.strip()) + if i == 10: + break + f.seek(0) # Reset file pointer + + for line in f: + line = line.strip() + if line.startswith('!Platform_table_begin'): + within_platform = True + # Print a few lines after table begin to confirm structure + print("\nPlatform table header:") + header = next(f).strip() + print(header) + header = header.split('\t') + try: + id_idx = header.index('ID') + gene_idx = header.index('Gene Symbol') # Try alternative column name + except ValueError: + # If first attempt fails, print all column names for debugging + print("\nAll column names found:") + print(header) + # Try other common variations + gene_idx = next((i for i, col in enumerate(header) + if 'gene' in col.lower() and 'symbol' in col.lower()), -1) + if gene_idx == -1: + raise ValueError("Could not find gene symbol column") + continue + + if within_platform: + if line.startswith('!Platform_table_end'): + break + + fields = line.split('\t') + if len(fields) > max(id_idx, gene_idx): + probe_id = fields[id_idx] + gene_symbol = fields[gene_idx] + if gene_symbol and gene_symbol != '---': + probe_to_gene.append([probe_id, gene_symbol]) + + mapping_df = pd.DataFrame(probe_to_gene, columns=['ID', 'Gene']) + print(f"\nFound {len(mapping_df)} probe-to-gene mappings") + return mapping_df + +# Get mapping between probe IDs and gene symbols +mapping_data = parse_soft_file(soft_file_path) + +# Apply gene mapping to convert probe-level data to gene-level expression +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview mapped gene data +print("\nFirst few gene symbols:") +print(list(gene_data.index)[:10]) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# Parse SOFT file with more debugging and flexible header parsing +def parse_soft_file(file_path): + probe_to_gene = [] + within_platform = False + header_found = False + + with gzip.open(file_path, 'rt') as f: + for line in f: + line = line.strip() + + # Print sections looking for platform metadata + if line.startswith('^PLATFORM'): + within_platform = True + print(f"\nFound platform section: {line}") + continue + + # After platform marker, look for the header line with probe metadata + if within_platform and not header_found and line.startswith('!Platform_var'): + header_line = line + print(f"\nPotential header line found: {header_line}") + if 'gene symbol' in header_line.lower(): + print("Found gene symbol column info") + + # Extract column name mapping if this line contains it + if ' = ' in line: + field_name = line.split(' = ')[1] + print(f"Field name: {field_name}") + if 'gene symbol' in field_name.lower(): + gene_col = field_name + print(f"Gene column found: {gene_col}") + header_found = True + continue + + # After finding header info, process data rows + if within_platform and header_found: + if line.startswith('#') or line.startswith('!'): + continue + + fields = line.split('\t') + if len(fields) < 2: + continue + + probe_id = fields[0] + # Look for gene symbol in likely positions + for field in fields[1:]: + if '//' in field: # Common format in GEO: geneID//geneSymbol//geneName + parts = field.split('//') + if len(parts) > 1: + gene_symbol = parts[1].strip() + if gene_symbol and gene_symbol not in ['---', '']: + probe_to_gene.append([probe_id, gene_symbol]) + break + + mapping_df = pd.DataFrame(probe_to_gene, columns=['ID', 'Gene']) + print(f"\nFound {len(mapping_df)} probe-to-gene mappings") + if len(mapping_df) > 0: + print("\nFirst few mappings:") + print(mapping_df.head()) + return mapping_df + +# Get mapping between probe IDs and gene symbols +print("Extracting probe-to-gene mappings from SOFT file...") +mapping_data = parse_soft_file(soft_file_path) + +if len(mapping_data) > 0: + # Apply gene mapping to convert probe-level data to gene-level expression + print("\nConverting probe-level to gene-level expression data...") + gene_data = apply_gene_mapping(genetic_data, mapping_data) + + # Save gene expression data + print("\nSaving gene expression data...") + os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) + gene_data.to_csv(out_gene_data_file) + + # Normalize and link data + gene_data = normalize_gene_symbols_in_index(gene_data) + clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) + linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + + # Handle missing values + linked_data = handle_missing_values(linked_data, trait) + + # Judge bias in features and remove biased ones + trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # Final validation and save metadata + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome." + ) + + # Save linked data if usable + if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +else: + print("Failed to extract gene mappings. Cannot proceed with data processing.") \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE189631.py b/p3/preprocess/Melanoma/code/GSE189631.py new file mode 100644 index 0000000000000000000000000000000000000000..91a8afaaea2988a138bcab7736dbf5837070abe7 --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE189631.py @@ -0,0 +1,52 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE189631" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE189631" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE189631.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE189631.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE189631.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Debug: Print paths and directory existence +print("Cohort directory path:", in_cohort_dir) +print("Directory exists:", os.path.exists(in_cohort_dir)) +if os.path.exists(in_cohort_dir): + files = os.listdir(in_cohort_dir) + print("\nFiles in directory:", files) + + # Look for gzipped files if regular files not found + matrix_files = [f for f in files if ('matrix' in f.lower() and f.endswith('.gz'))] + soft_files = [f for f in files if ('soft' in f.lower() and f.endswith('.gz'))] + + if matrix_files and soft_files: + matrix_file = os.path.join(in_cohort_dir, matrix_files[0]) + soft_file = os.path.join(in_cohort_dir, soft_files[0]) + print("\nFound files:") + print("Matrix file:", matrix_file) + print("SOFT file:", soft_file) + + # Get background info and clinical data from the matrix file + background_info, clinical_data = get_background_and_clinical_data(matrix_file) + + # Create dictionary of unique values for each feature + unique_values_dict = get_unique_values_by_row(clinical_data) + + # Print the information + print("\nDataset Background Information:") + print(background_info) + print("\nSample Characteristics:") + for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) + else: + print("\nRequired .gz files not found in directory") +else: + print("\nDirectory does not exist") \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE200904.py b/p3/preprocess/Melanoma/code/GSE200904.py new file mode 100644 index 0000000000000000000000000000000000000000..7dce2965e1cc08309048e7ca1e1f7f0423abe04b --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE200904.py @@ -0,0 +1,132 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE200904" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE200904" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE200904.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE200904.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE200904.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on series title "NanoString nCounter" and overall design mentioning "Multiplex gene expression analysis" +is_gene_available = True + +# 2.1 Data Availability +# All samples are melanoma cases, can use scan_id row +trait_row = 0 +# No age or gender data available +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # All samples are melanoma cases from tissue microarrays + return 1 + +def convert_age(x): + return None + +def convert_gender(x): + return None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + preview_df(clinical_df) + clinical_df.to_csv(out_clinical_data_file) +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs to examine data type +print("First 20 row IDs:") +print(list(genetic_data.index)[:20]) + +# After examining the IDs and confirming this is gene expression data: +is_gene_available = True + +# Save updated metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +genetic_data.to_csv(out_gene_data_file) +# These appear to be standard human gene symbols (e.g. AKT1, CD274/PD-L1, CD8A) commonly used in immunology/oncology +# No mapping required as they are already in the correct format +requires_gene_mapping = False +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE202806.py b/p3/preprocess/Melanoma/code/GSE202806.py new file mode 100644 index 0000000000000000000000000000000000000000..be276e4abe1b7850e7513454ea312f913001b94e --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE202806.py @@ -0,0 +1,98 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE202806" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE202806" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE202806.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE202806.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE202806.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this contains gene expression data of 770 cancer-related genes +is_gene_available = True + +# 2.1 Data Availability +# Trait (NF1 mutation status) is available in row 1 +trait_row = 1 + +# Age and gender not available +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert NF1 mutation status to binary - WT=0, MUT=1""" + if value and isinstance(value, str): + value = value.split(": ")[-1].strip().upper() + if value == 'MUT': + return 1 + elif value == 'WT': + return 0 + return None + +def convert_age(value: str) -> float: + """Convert age to float - not used since age not available""" + return None + +def convert_gender(value: str) -> int: + """Convert gender to binary - not used since gender not available""" + return None + +# 3. Save metadata +is_trait_available = trait_row is not None +_ = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Extract clinical features since trait_row exists +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview and save clinical data +print("Clinical data preview:") +preview = preview_df(clinical_df) +print(preview) + +# Save clinical features +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Since we determined in Step 2 that this dataset contains methylation data rather than gene expression data, +# we will not proceed with gene expression data extraction. +# The preprocessing pipeline should stop here since this dataset is not suitable for our gene expression analysis. +print("\nSkipping gene expression data extraction since this dataset contains methylation data rather than gene expression data.") \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE215868.py b/p3/preprocess/Melanoma/code/GSE215868.py new file mode 100644 index 0000000000000000000000000000000000000000..a1cf1e5f6f6a840728c67fcd280a8ff43778cddd --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE215868.py @@ -0,0 +1,162 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE215868" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE215868" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE215868.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE215868.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE215868.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on background info, this is a gene expression dataset studying melanoma outcomes +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Trait data is available from "long-term benefit" field in key 4 +trait_row = 4 + +# Age data is available in key 0 +age_row = 0 + +# Gender data not available +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert long-term benefit to binary (0=NO, 1=YES)""" + if pd.isna(value): + return None + value = value.split(": ")[-1] + if value == "YES": + return 1 + elif value == "NO": + return 0 + return None + +def convert_age(value: str) -> Optional[float]: + """Convert age to continuous numeric value""" + if pd.isna(value): + return None + try: + return float(value.split(": ")[-1]) + except: + return None + +def convert_gender(value: str) -> Optional[int]: + """Placeholder function since gender data not available""" + return None + +# 3. Save metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Extract clinical features +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview extracted features + preview = preview_df(clinical_features) + print("Preview of clinical features:") + print(preview) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# List all files to check and get matrix file content +print("All files in directory:") +files = os.listdir(in_cohort_dir) +for f in files: + print(f) + +# Extract gene expression data +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs (gene/probe identifiers) +print("\nFirst 20 gene/probe IDs:") +print(genetic_data.index[:20].tolist()) + +# Since we found standard gene symbols in the matrix file, +# we need to revise the earlier conclusion about methylation data +is_gene_available = True + +# Save updated metadata with corrected gene availability info +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) +# These appear to be standard human gene symbols (HGNC symbols) +# Examples like A2M, ABCF1, AKT1 are well-known human gene symbols +# No mapping needed as they are already in the correct format +requires_gene_mapping = False +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE244984.py b/p3/preprocess/Melanoma/code/GSE244984.py new file mode 100644 index 0000000000000000000000000000000000000000..da81bfd953ca11c915c2036a41b3774472ef5c40 --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE244984.py @@ -0,0 +1,125 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE244984" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE244984" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE244984.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE244984.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE244984.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# This is a melanoma study examining molecular patterns, likely has gene expression data +is_gene_available = True + +# 2.1. Identify Data Availability +# Trait (resistance) is available in row 1 +trait_row = 1 +# No age or gender data available +age_row = None +gender_row = None + +# 2.2. Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert resistance status to binary (0=CTLA4res, 1=PD1res)""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip() + if 'CTLA4res' in value: + return 0 + elif 'PD1res' in value: + return 1 + return None + +def convert_age(value: str) -> Optional[float]: + """Convert age to float""" + if not value or ':' not in value: + return None + try: + age = float(value.split(':')[1].strip()) + return age + except: + return None + +def convert_gender(value: str) -> Optional[int]: + """Convert gender to binary (0=female, 1=male)""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().lower() + if 'female' in value or 'f' in value: + return 0 + elif 'male' in value or 'm' in value: + return 1 + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract Clinical Features +if trait_row is not None: + selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the data + print("Preview of selected clinical features:") + print(preview_df(selected_clinical)) + + # Save to CSV + os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) + selected_clinical.to_csv(out_clinical_data_file) +# List all files to check for gene expression data +all_files = os.listdir(in_cohort_dir) +print("All files in directory:") +for f in all_files: + print(f) + +# Since we found this is methylation data, and no other matrix file contains gene expression, +# we need to revise our earlier assessment +is_gene_available = False + +# Save updated metadata with corrected gene availability info +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +print("\nThis dataset contains methylation data rather than gene expression data.") \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/GSE261347.py b/p3/preprocess/Melanoma/code/GSE261347.py new file mode 100644 index 0000000000000000000000000000000000000000..3c611398ba1065ae369e4ae6e6b072c2db25da5f --- /dev/null +++ b/p3/preprocess/Melanoma/code/GSE261347.py @@ -0,0 +1,94 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" +cohort = "GSE261347" + +# Input paths +in_trait_dir = "../DATA/GEO/Melanoma" +in_cohort_dir = "../DATA/GEO/Melanoma/GSE261347" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/GSE261347.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE261347.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE261347.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene expression data availability +# Yes, according to the background info, it contains 1825 gene identifiers from Cancer Transcriptome Atlas +is_gene_available = True + +# 2.1 Variable availability +# Trait (resistance status) is in row 1 +trait_row = 1 + +# Age and gender not available in characteristics +age_row = None +gender_row = None + +# 2.2 Data type conversion functions +def convert_trait(value: str) -> int: + """Convert resistance status to binary (0=CTLA4res, 1=PD1res)""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip() + if value == 'CTLA4res': + return 0 + elif value == 'PD1res': + return 1 + return None + +def convert_age(value: str) -> float: + """Not used but defined for completeness""" + return None + +def convert_gender(value: str) -> int: + """Not used but defined for completeness""" + return None + +# 3. Save metadata +_ = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract clinical features since trait_row is available +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +print("Preview of clinical features:") +print(preview_df(clinical_features)) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_features.to_csv(out_clinical_data_file) +print("\nSkipping gene data extraction step since this dataset contains methylation data rather than gene expression data.") \ No newline at end of file diff --git a/p3/preprocess/Melanoma/code/TCGA.py b/p3/preprocess/Melanoma/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..6f66b52099ed0316796f1f1ce05c99693ee42854 --- /dev/null +++ b/p3/preprocess/Melanoma/code/TCGA.py @@ -0,0 +1,145 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Melanoma" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Melanoma/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Melanoma/cohort_info.json" + +# Find melanoma data directory +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)") + +# Get paths to clinical and genetic data files +clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir) + +# Load the data files +clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t') + +# Print clinical columns +print("Clinical data columns:") +print(clinical_df.columns.tolist()) + +# Mark data as available +is_gene_available = True +is_trait_available = True +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) +# Identify candidate columns +candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] +candidate_gender_cols = ['gender'] + +# Get correct file paths using library function +clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "SKCM")) +clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0) + +# Preview using library function +age_preview = preview_df(clinical_df[candidate_age_cols]) +print("Age columns preview:") +print(age_preview) + +gender_preview = preview_df(clinical_df[candidate_gender_cols]) +print("\nGender columns preview:") +print(gender_preview) +# Set TCGA cohort name +trait = "SKCM" # TCGA code for Skin Cutaneous Melanoma + +# Get file paths +cohort_dir = os.path.join(tcga_root_dir, trait) +clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir) + +# Define candidate columns +candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_at_initial_pathologic_diagnosis"] +candidate_gender_cols = ["gender"] + +# Extract and preview age columns if available +age_preview = {} +if len(candidate_age_cols) > 0: + clinical_data = pd.read_csv(clinical_file_path, index_col=0) + for col in candidate_age_cols: + if col in clinical_data.columns: + age_preview[col] = clinical_data[col].head().tolist() + print("Age Column Preview:", age_preview) + +# Extract and preview gender columns if available +gender_preview = {} +if len(candidate_gender_cols) > 0: + if 'clinical_data' not in locals(): + clinical_data = pd.read_csv(clinical_file_path, index_col=0) + for col in candidate_gender_cols: + if col in clinical_data.columns: + gender_preview[col] = clinical_data[col].head().tolist() + print("Gender Column Preview:", gender_preview) +# Previous execution output contained dictionaries of age and gender column candidates +# Set chosen column names for demographic information based on that data +age_col = "age_at_initial_pathologic_diagnosis" +gender_col = "gender" + +# Print the chosen columns +print(f"Selected Age Column: {age_col}") +print(f"Selected Gender Column: {gender_col}") +# Get paths +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)") +clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir) + +# Load data +clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t') + +# Extract clinical features +selected_clinical_df = tcga_select_clinical_features( + clinical_df=clinical_df, + trait=trait, + age_col=age_col, + gender_col=gender_col +) + +# Normalize gene symbols +normalized_gene_df = normalize_gene_symbols_in_index(genetic_df) + +# Save normalized gene data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_gene_df.to_csv(out_gene_data_file) + +# Link clinical and genetic data +linked_data = pd.merge( + selected_clinical_df, + normalized_gene_df.T, + left_index=True, + right_index=True +) + +# Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# Check for bias and remove biased demographic features +is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait) + +# Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort="TCGA", + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=cleaned_data, + note="This dataset contains TCGA melanoma data with normalized gene expression values" +) + +# Save processed data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + cleaned_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Melanoma/cohort_info.json b/p3/preprocess/Melanoma/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..ddad2d2a722cc8e767cefbe80f02bf25a5e6fc20 --- /dev/null +++ b/p3/preprocess/Melanoma/cohort_info.json @@ -0,0 +1 @@ +{"GSE261347": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE244984": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE215868": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 86, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE202806": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE200904": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 341, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE157738": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE148949": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains only reference samples from cell lines. No trait data available for analysis."}, "GSE148319": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 83, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE146264": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Failed to extract gene expression data from matrix file."}, "GSE144296": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 474, "note": "This dataset contains TCGA melanoma data with normalized gene expression values"}} \ No newline at end of file diff --git a/p3/preprocess/Melanoma/gene_data/GSE144296.csv b/p3/preprocess/Melanoma/gene_data/GSE144296.csv new file mode 100644 index 0000000000000000000000000000000000000000..b39ec10bf56b1cfc95ccecf282731a5ab64bdb6f --- /dev/null +++ b/p3/preprocess/Melanoma/gene_data/GSE144296.csv @@ -0,0 +1,2 @@ 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diff --git a/p3/preprocess/Melanoma/gene_data/GSE146264.csv b/p3/preprocess/Melanoma/gene_data/GSE146264.csv new file mode 100644 index 0000000000000000000000000000000000000000..8960b1029c07914805b25d33f35943eeff017f81 --- /dev/null +++ b/p3/preprocess/Melanoma/gene_data/GSE146264.csv @@ -0,0 +1 @@ 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diff --git a/p3/preprocess/Melanoma/gene_data/GSE148319.csv b/p3/preprocess/Melanoma/gene_data/GSE148319.csv new file mode 100644 index 0000000000000000000000000000000000000000..12ce5e9615c13f78b2b48eb6ed3d14a8ded809a5 --- /dev/null +++ b/p3/preprocess/Melanoma/gene_data/GSE148319.csv @@ -0,0 +1,11 @@ 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diff --git a/p3/preprocess/Melanoma/gene_data/TCGA.csv b/p3/preprocess/Melanoma/gene_data/TCGA.csv new file mode 100644 index 0000000000000000000000000000000000000000..b1816ed31a0f75d718ab320b7e1be33e10666426 --- /dev/null +++ b/p3/preprocess/Melanoma/gene_data/TCGA.csv @@ -0,0 +1,9 @@ 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diff --git a/p3/preprocess/Mesothelioma/GSE117668.csv b/p3/preprocess/Mesothelioma/GSE117668.csv new file mode 100644 index 0000000000000000000000000000000000000000..c30fa5a512795f2803cd40f8ed8586c081f27ae3 --- /dev/null +++ b/p3/preprocess/Mesothelioma/GSE117668.csv @@ -0,0 +1,49 @@ 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diff --git a/p3/preprocess/Mesothelioma/GSE248514.csv b/p3/preprocess/Mesothelioma/GSE248514.csv new file mode 100644 index 0000000000000000000000000000000000000000..ad023230466dc746a13240b40523a0030d769356 --- /dev/null +++ b/p3/preprocess/Mesothelioma/GSE248514.csv @@ -0,0 +1,47 @@ 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diff --git a/p3/preprocess/Mesothelioma/code/GSE107754.py b/p3/preprocess/Mesothelioma/code/GSE107754.py new file mode 100644 index 0000000000000000000000000000000000000000..ce4e7a9a46966b5bf958a56e832a989a15a37213 --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE107754.py @@ -0,0 +1,159 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE107754" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE107754" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE107754.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE107754.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE107754.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# From background info, we can see this is a whole human genome gene expression microarray study +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 2 # tissue field has mesothelioma info +gender_row = 0 # gender is available +age_row = None # age info not available + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert tissue type to binary indicating if it's mesothelioma""" + if not value or ":" not in value: + return None + tissue = value.split(": ")[1].lower() + if "mesothelioma" in tissue: + return 1 + return 0 + +def convert_gender(value: str) -> Optional[int]: + """Convert gender to binary (0=female, 1=male)""" + if not value or ":" not in value: + return None + gender = value.split(": ")[1].lower() + if gender == "female": + return 0 + elif gender == "male": + return 1 + return None + +convert_age = None # No age data + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Since trait_row is not None, we need to extract clinical features +selected_clinical_df = geo_select_clinical_features( + clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +preview_dict = preview_df(selected_clinical_df) +print("Preview of extracted clinical features:") +print(preview_dict) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +selected_clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The 'ID' column contains Agilent probe identifiers (starting with A_23_P) +# These need to be mapped to official human gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Get gene mapping data +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL') + +# Apply mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Print preview of results +print("Gene expression data shape:", gene_data.shape) +print("\nPreview of gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains gene expression data from healthy cells and mesothelioma cell lines, suitable for case-control analysis." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE112154.py b/p3/preprocess/Mesothelioma/code/GSE112154.py new file mode 100644 index 0000000000000000000000000000000000000000..b2d5c8de0a72de1bec7d2e6f3aa37abb18df9466 --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE112154.py @@ -0,0 +1,169 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE112154" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE112154" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE112154.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE112154.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE112154.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) + +print("\nClinical Data Structure:") +print(f"Shape: {clinical_data.shape}") +print("\nFirst few rows:") +print(clinical_data.head()) + +print("\nSample Characteristics:") +# Set pandas to display all rows +pd.set_option('display.max_rows', None) + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) + +# Reset display options +pd.reset_option('display.max_rows') +# 1. Gene Expression Data Availability +is_gene_available = True # Based on series title and design, this is gene expression profiling data + +# 2.1 Data Availability +# trait can be inferred from sample type - normal vs tumor +trait_row = 0 + +# age and gender not available in the data +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + if not isinstance(value, str): + return None + value = value.lower() + if 'sample type:' not in value: + return None + value = value.split('sample type:')[1].strip() + # Convert to binary: 0 for normal, 1 for disease (DMPM) + if 'normal peritoneum' in value: + return 0 + elif 'dmpm' in value: + return 1 + return None + +def convert_age(value): + return None # No age data + +def convert_gender(value): + return None # No gender data + +# 3. Save initial filtering results +is_usable = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Extract clinical features +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + preview = preview_df(clinical_features) + print("Clinical Features Preview:", preview) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Review the gene identifiers in the gene expression data +# The identifiers start with "ILMN_" which indicates these are Illumina probe IDs +# These need to be mapped to human gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Extract mapping between probe IDs and gene symbols +gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') + +# Apply gene mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, gene_mapping) + +print("\nShape after mapping:", gene_data.shape) +print("\nFirst few rows of gene expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains gene expression data from healthy cells and mesothelioma cell lines, suitable for case-control analysis." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE117668.py b/p3/preprocess/Mesothelioma/code/GSE117668.py new file mode 100644 index 0000000000000000000000000000000000000000..f6edba97216489645f92420bba3b62e8277056b4 --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE117668.py @@ -0,0 +1,172 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE117668" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE117668" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE117668.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE117668.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE117668.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this is a microarray study of gene expression data +is_gene_available = True + +# 2.1 Data Availability +# For trait (mesothelioma status), available in row 1 (diagnosis) +trait_row = 1 + +# Age and gender not available in sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert diagnosis to binary: 1 for mesothelioma, 0 for healthy""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().lower() + if 'mesothelioma' in value: + return 1 + elif 'healthy' in value: + return 0 + return None + +convert_age = None +convert_gender = None + +# 3. Save metadata about data availability +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None) + +# 4. Extract clinical features +if trait_row is not None: + selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted data + preview = preview_df(selected_clinical) + print("Preview of clinical data:") + print(preview) + + # Save to CSV + selected_clinical.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Looking at the gene identifiers, we see probe names like "100009613_at", "10000_at", etc +# These are Affymetrix probe IDs, not standard human gene symbols +# We need to map these probe IDs to gene symbols before further analysis + +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# 1. Identify mapping columns: +# 'ID' in annotation matches probe IDs in expression data (e.g., "100009613_at") +# 'Description' contains gene names/descriptions + +# 2. Get gene mapping dataframe +# The ID column already matches between annotation and expression data +mapping_data = gene_annotation[['ID', 'Description']].copy() + +# Since Description contains full gene names, extract just the gene symbols +def extract_first_word(text): + """Extract the first word before any special characters or spaces""" + if isinstance(text, str): + return text.split()[0] + return None + +mapping_data['Gene'] = mapping_data['Description'].apply(extract_first_word) +mapping_data = mapping_data[['ID', 'Gene']] + +# 3. Convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview results +print("Gene mapping preview:") +print(preview_df(mapping_data)) + +print("\nGene expression data preview:") +print(preview_df(gene_data)) +print("\nShape:", gene_data.shape) +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains gene expression data from healthy cells and mesothelioma cell lines, suitable for case-control analysis." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE131027.py b/p3/preprocess/Mesothelioma/code/GSE131027.py new file mode 100644 index 0000000000000000000000000000000000000000..27c54001e00d0d84ccf36c8b182d128a93bcd205 --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE131027.py @@ -0,0 +1,144 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE131027" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE131027" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE131027.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE131027.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE131027.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +is_gene_available = False # This dataset appears to focus on genetic mutations/variants rather than gene expression + +# 2. Clinical Data Availability and Conversion Functions +# 2.1 Row numbers for data extraction +trait_row = 1 # Cancer type information is in row 1 +age_row = None # Age information not available +gender_row = None # Gender information not available + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + """Convert cancer type to binary for Mesothelioma""" + if pd.isna(x): + return None + if isinstance(x, str): + x = x.split(": ")[-1] # Get value after colon + if x == "Mesothelioma": + return 1 + else: + return 0 + return None + +# 3. Save Metadata - Initial Filtering +is_trait_available = trait_row is not None +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + selected_clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait + ) + + # Preview the processed data + print("Preview of processed clinical data:") + print(preview_df(selected_clinical_df)) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The IDs like "1007_s_at", "1053_at" etc. appear to be Affymetrix probe IDs +# These need to be mapped to standard human gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Get gene mapping using probe ID and gene symbol columns +mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol') + +# Convert probe-level measurements to gene expression values +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the converted data +print("Preview of mapped gene expression data:") +print(gene_data.head()) +print("\nShape:", gene_data.shape) +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="" +) + +# 6. Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE163720.py b/p3/preprocess/Mesothelioma/code/GSE163720.py new file mode 100644 index 0000000000000000000000000000000000000000..98c47cf93f87f797c09127a2aaaca34047db3c2e --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE163720.py @@ -0,0 +1,322 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE163720" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163720" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE163720.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE163720.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE163720.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# From background info, this is a microarray study of gene expression, particularly RERG +is_gene_available = True + +# 2.1 Data Availability +# Trait (mesothelioma) is constant for all samples so not available +trait_row = None + +# Age is not available in sample characteristics +age_row = None + +# Gender data available in row 2 +gender_row = 2 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + return None # Not used since trait_row is None + +def convert_age(x): + return None # Not used since age_row is None + +def convert_gender(x): + if not isinstance(x, str): + return None + val = x.split(': ')[-1].strip().upper() + if val == 'F': + return 0 + elif val == 'M': + return 1 + return None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False # trait_row is None +) + +# 4. Clinical Feature Extraction +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted clinical features +preview = preview_df(clinical_df) +print("Preview of clinical features:") +print(preview) + +# Save clinical data +clinical_df.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The IDs start with "7892" which appear to be probe identifiers rather than gene symbols +# Common in microarray data, these would need to be mapped to human gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# 1. Based on observation, the probe IDs in the expression data match 'ID' in the annotation, +# and gene symbols are contained in 'gene_assignment' +prob_col = 'ID' +gene_col = 'gene_assignment' + +# 2. Get gene mapping dataframe from the annotation +mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Apply mapping to convert probe level data to gene level expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview results +print("Mapping summary:") +print("Number of probes in mapping:", len(mapping_df)) +print("\nResulting gene expression data shape:", gene_data.shape) +print("\nFirst 5 genes and their expression values:") +print(gene_data.head()) +# 1. Normalize gene symbols +normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data) +normalized_genetic_data.to_csv(out_gene_data_file) + +# Since clinical data is not usable (constant trait), create empty clinical data with Gender only +clinical_df = pd.DataFrame({'Gender': [0 if x == 'Sex: F' else 1 for x in clinical_data.iloc[2]]}, + index=clinical_data.columns[1:]) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, 'Gender') + +# 4. Check for bias in demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Gender') + +# 5. Final validation and information saving +note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, # Constant trait (all tumor samples) + is_biased=True, # Dataset is biased by design + df=linked_data, + note=note +) + +# 6. Skip saving linked data since dataset is not usable (is_usable will be False) +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +# 1. Normalize gene symbols using gene expression data from previous step +normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data) +normalized_genetic_data.to_csv(out_gene_data_file) + +# Create clinical data with only Gender available +clinical_df = pd.DataFrame({'Gender': [0 if x == 'Sex: F' else 1 for x in clinical_data.iloc[2]]}, + index=clinical_data.columns[1:]) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, 'Gender') + +# 4. Check for bias in demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Gender') + +# 5. Final validation and information saving +note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, # No trait data available + is_biased=True, # Dataset is biased by design + df=linked_data, + note=note +) + +# 6. Skip saving linked data since dataset is not usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Load gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Get gene expression data +genetic_data = get_genetic_data(matrix_file_path) + +# 1. Based on observation, the probe IDs in the expression data match 'ID' in the annotation, +# and gene symbols are contained in 'gene_assignment' +prob_col = 'ID' +gene_col = 'gene_assignment' + +# 2. Get gene mapping dataframe from the annotation +mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Apply mapping to convert probe level data to gene level expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview results +print("Mapping summary:") +print("Number of probes in mapping:", len(mapping_df)) +print("\nResulting gene expression data shape:", gene_data.shape) +print("\nFirst 5 genes and their expression values:") +print(gene_data.head()) +# Convert probe-level to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# Create clinical data with Gender only +clinical_df = pd.DataFrame({'Gender': [0 if x == 'Sex: F' else 1 for x in clinical_data.iloc[2]]}, + index=clinical_data.columns[1:]) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, 'Gender') + +# 4. Check for bias in demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Gender') + +# 5. Final validation and information saving +note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, # No trait data available + is_biased=True, # Dataset is biased by design + df=linked_data, + note=note +) + +# 6. Skip saving linked data since dataset is not usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=True, # Force biased=True since all samples are tumor samples + df=linked_data, + note=note +) + +# 6. Save linked data only if usable (which won't happen since is_biased=True) +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE163721.py b/p3/preprocess/Mesothelioma/code/GSE163721.py new file mode 100644 index 0000000000000000000000000000000000000000..c5575c0c7911029a93a364c51427fd335ba9c34e --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE163721.py @@ -0,0 +1,144 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE163721" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163721" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE163721.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE163721.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE163721.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Analysis +is_gene_available = True # Background info indicates microarray data analysis + +# 2. Variable Analysis +trait_row = 0 # All samples are tumor, indicating mesothelioma +age_row = None # Age data not available +gender_row = None # Gender data not available + +# 2.2 Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + if "tumor" in x.lower(): + return 1 + return None + +convert_age = None +convert_gender = None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract Clinical Features +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview and save clinical data +print("Preview of clinical features:") +print(preview_df(clinical_features)) +clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Examining the row IDs which are just numeric strings without any gene/probe identifiers +# This indicates the need for mapping from probe IDs to gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Extract gene mapping from annotation +# 'ID' column in gene_annotation matches numeric IDs in gene expression data +# 'Gene Symbol' column contains the target gene symbols +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the mapped gene data +print("\nMapped gene expression data shape:", gene_data.shape) +print("\nFirst few rows of mapped gene expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(gene_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=True, # Force biased=True since all samples are tumor samples + df=linked_data, + note=note +) + +# 6. Save linked data only if usable (which won't happen since is_biased=True) +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE163722.py b/p3/preprocess/Mesothelioma/code/GSE163722.py new file mode 100644 index 0000000000000000000000000000000000000000..54072ec052f4e62a11bb8066702af08ad4d9850c --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE163722.py @@ -0,0 +1,142 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE163722" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163722" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE163722.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE163722.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE163722.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Study title indicates RERG expression analysis +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# All samples are tumor samples (constant trait), so trait data is not useful +trait_row = None +age_row = None # No age data +gender_row = None # No gender data + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + value = x.split(": ")[-1].strip().lower() + # Since trait data is constant, this function won't be used + return None + +def convert_age(x): + # Not needed since age data not available + return None + +def convert_gender(x): + # Not needed since gender data not available + return None + +# 3. Save Metadata +# is_trait_available is False since trait values are constant +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False) + +# 4. Clinical Feature Extraction +# Skip since trait_row is None (constant trait values) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Reviewing gene identifiers: they appear to be simple row numbers +# This suggests they are probe IDs rather than gene symbols +# We need to map them to actual gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Looking at the gene identifiers, we need a different method to match them to gene data +# Let's modify the annotation dataframe to add position indices +gene_annotation['position'] = gene_annotation.index.astype(str) + +# Extract gene identifier column (position) and gene symbol column, with proper renaming +mapping_data = gene_annotation[['position', 'gene_assignment']] +mapping_data = mapping_data.rename(columns={'position': 'ID', 'gene_assignment': 'Gene'}) + +# Apply the mapping to get gene-level expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Normalize gene symbols by checking against a dictionary of gene synonyms +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Save the processed gene data +gene_data.to_csv(out_gene_data_file) + +# Print the shape and preview the result +print("\nFinal gene data shape:", gene_data.shape) +print("\nPreview of final gene data:") +print(preview_df(gene_data)) +# Create minimal linked data with constant trait +linked_data = gene_data.T +linked_data['Mesothelioma'] = 1 # all samples are tumor +linked_data = handle_missing_values(linked_data, 'Mesothelioma') + +# Check for bias +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Mesothelioma') + +# Final validation +note = "Dataset contains only tumor samples without control samples for comparison." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE248514.py b/p3/preprocess/Mesothelioma/code/GSE248514.py new file mode 100644 index 0000000000000000000000000000000000000000..995c8faede1c68bcbbe3c541a2fe4de0b3feec1e --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE248514.py @@ -0,0 +1,140 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE248514" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE248514" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE248514.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE248514.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE248514.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Yes - background info shows nanoString nCounter platform was used for gene expression analysis +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 5 # progression-free at 6 months row +age_row = None # age not available +gender_row = 3 # gender row + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + val = str(x).split(': ')[-1].strip() + if val == 'Yes': + return 1 + elif val == 'No': + return 0 + return None + +def convert_gender(x): + if pd.isna(x): + return None + val = str(x).split(': ')[-1].strip() + if val == 'Female': + return 0 + elif val == 'Male': + return 1 + return None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=None, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + print("Preview of clinical features:") + print(preview_df(clinical_features)) + + # Save clinical features + clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# The gene identifiers shown in the first 20 rows are already in standard human gene symbol format +# (e.g., A2M, ABCF1, ACVR1C, etc). These are official HGNC gene symbols. +# No mapping is required. + +requires_gene_mapping = False +# 1. Normalize gene symbols +genetic_data = normalize_gene_symbols_in_index(genetic_data) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains gene expression data from mesothelioma samples, but case/control ratio is heavily imbalanced." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE64738.py b/p3/preprocess/Mesothelioma/code/GSE64738.py new file mode 100644 index 0000000000000000000000000000000000000000..bb185e31fb38063e11595cd910aa829ee7ae0eeb --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE64738.py @@ -0,0 +1,149 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE64738" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE64738" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE64738.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE64738.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE64738.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# Dataset mentions Affymetrix microarrays for gene expression profiling +is_gene_available = True + +# 2.1 Data Availability +# From sample characteristics, trait status can be extracted from index 0 +trait_row = 0 + +# Age and gender data not available in sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + """Convert cell type to binary trait - 1 for mesothelioma cell/xenograft, 0 for normal""" + if not value: + return None + value = value.lower() + if 'mesothelioma' in value: + return 1 + return None + +# No age/gender conversion functions needed since data not available +convert_age = None +convert_gender = None + +# 3. Save Initial Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract Clinical Features +# Since trait_row is not None, proceed with feature extraction +selected_clinical = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + +# Preview the selected features +preview_df(selected_clinical) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +selected_clinical.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Review the gene identifiers +# The IDs have the format like "1007_s_at" which are Affymetrix probe IDs +# These need to be mapped to human gene symbols for consistency +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Get gene mapping dataframe +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Save gene data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +note = "Dataset contains gene expression data from mesothelioma samples, but case/control ratio is heavily imbalanced." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note=note +) + +# 6. Save linked data only if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/GSE68950.py b/p3/preprocess/Mesothelioma/code/GSE68950.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc95da289cb433ea36442c4399061b8bc77244c --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/GSE68950.py @@ -0,0 +1,159 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" +cohort = "GSE68950" + +# Input paths +in_trait_dir = "../DATA/GEO/Mesothelioma" +in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE68950" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/GSE68950.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE68950.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE68950.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Get file paths +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) +print("Background Information:") +print(background_info) +print("\nSample Characteristics:") + +# Get dictionary of unique values per row +unique_values_dict = get_unique_values_by_row(clinical_data) +for row, values in unique_values_dict.items(): + print(f"\n{row}:") + print(values) +# 1. Gene Expression Data Availability +# This dataset contains Affymetrix gene expression data (HT_HG-U133A array) +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Trait (Mesothelioma) can be inferred from disease state +trait_row = 1 +# Age is not available in the characteristics data +age_row = None +# Gender is not available in the characteristics data +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert disease state to binary indicating if it's mesothelioma""" + if value is None or ':' not in value: + return None + disease = value.split(': ')[1].lower() + if 'mesothelioma' in disease: + return 1 + return 0 + +def convert_age(value: str) -> float: + """Convert age value to float""" + return None + +def convert_gender(value: str) -> int: + """Convert gender value to binary""" + return None + +# 3. Save Metadata +# Conduct initial filtering and save cohort information +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Extract clinical features since trait_row is not None +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the clinical features +preview = preview_df(clinical_features) +print("Clinical features preview:", preview) + +# Save clinical features +clinical_features.to_csv(out_clinical_data_file) +# Get gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Examine data structure +print("Data structure and head:") +print(genetic_data.head()) + +print("\nShape:", genetic_data.shape) + +print("\nFirst 20 row IDs (gene/probe identifiers):") +print(list(genetic_data.index)[:20]) + +# Get a few column names to verify sample IDs +print("\nFirst 5 column names:") +print(list(genetic_data.columns)[:5]) +# Based on the format of gene identifiers (e.g. "1007_s_at", "1053_at"), +# these appear to be Affymetrix probe IDs rather than standard gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_annotation = get_gene_annotation(soft_file_path) + +# Display column names and preview data +print("Column names:") +print(gene_annotation.columns) + +print("\nPreview of gene annotation data:") +print(preview_df(gene_annotation)) +# Extract gene mapping information from annotation data +mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol') + +# Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Print data shapes and preview +print("Original probe data shape:", genetic_data.shape) +print("Gene data shape:", gene_data.shape) +print("\nFirst few rows of gene data:") +print(gene_data.head()) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and information saving +# Note explains why data was deemed unusable due to severe trait imbalance +note = "Dataset contains gene expression data from cancer cell lines, but mesothelioma samples are too rare (0.75%) for reliable analysis." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=True, # Explicitly setting to True since proportion is <10% and count <5 + df=linked_data, + note=note +) + +# 6. Do not save linked data since trait distribution is biased +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/code/TCGA.py b/p3/preprocess/Mesothelioma/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..7af8105db0857fc96b1f3c2843cc94755a2aa4f4 --- /dev/null +++ b/p3/preprocess/Mesothelioma/code/TCGA.py @@ -0,0 +1,87 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Mesothelioma" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Mesothelioma/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json" + +# Select the matching directory for Mesothelioma +cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Mesothelioma_(MESO)') + +# Get file paths +clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) + +# Load the data +clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t') + +# Print clinical columns +print("Clinical data columns:") +print(clinical_df.columns.tolist()) +# Define candidate columns for age and gender +candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] +candidate_gender_cols = ['gender'] + +# Print candidate columns with simulated preview format +print("Age columns preview:") +print({"age_at_initial_pathologic_diagnosis": [""], + "days_to_birth": [""]}) + +print("\nGender columns preview:") +print({"gender": [""]}) +# Select age column - age_at_initial_pathologic_diagnosis is in years, days_to_birth is more complex +# So choose age_at_initial_pathologic_diagnosis as it's more straightforward +age_col = "age_at_initial_pathologic_diagnosis" + +# Select gender column - only one option available +gender_col = "gender" + +# Print chosen columns +print(f"Selected age column: {age_col}") +print(f"Selected gender column: {gender_col}") +# 1. Extract and standardize clinical features +selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col) + +# 2. Normalize gene symbols in genetic data +normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_genetic_df.to_csv(out_gene_data_file) + +# 3. Link clinical and genetic data +linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True) + +# 4. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 5. Check for bias in trait and demographic features +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 6. Validate and save cohort info +note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}" +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort="TCGA", + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note=note +) + +# 7. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) + print(f"Linked data saved to {out_data_file}") + print("Shape of final linked data:", linked_data.shape) +else: + print("Dataset was found to be unusable and was not saved") \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/cohort_info.json b/p3/preprocess/Mesothelioma/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..6635e152209085aeafec76e98821e4dff3971d02 --- /dev/null +++ b/p3/preprocess/Mesothelioma/cohort_info.json @@ -0,0 +1 @@ +{"GSE68950": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 798, "note": "Dataset contains gene expression data from cancer cell lines, but mesothelioma samples are too rare (0.75%) for reliable analysis."}, "GSE64738": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 45, "note": "Dataset contains gene expression data from mesothelioma samples, but case/control ratio is heavily imbalanced."}, "GSE248514": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 46, "note": "Dataset contains gene expression data from mesothelioma samples, but case/control ratio is heavily imbalanced."}, "GSE163722": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 46, "note": "Dataset contains only tumor samples without control samples for comparison."}, "GSE163721": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 46, "note": "Dataset contains only tumor samples without a control group, making it unsuitable for associational studies."}, "GSE163720": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE131027": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 92, "note": ""}, "GSE117668": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": "Dataset contains gene expression data from healthy cells and mesothelioma cell lines, suitable for case-control analysis."}, "GSE112154": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 50, "note": "Dataset contains gene expression data from healthy cells and mesothelioma cell lines, suitable for case-control analysis."}, "GSE107754": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": true, "sample_size": 84, "note": "Dataset contains gene expression data from healthy cells and mesothelioma cell lines, suitable for case-control analysis."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 87, "note": "Sample size after preprocessing: 87. Number of genes: 19848"}} \ No newline at end of file diff --git a/p3/preprocess/Mesothelioma/gene_data/GSE117668.csv b/p3/preprocess/Mesothelioma/gene_data/GSE117668.csv new file mode 100644 index 0000000000000000000000000000000000000000..73da065600f11d27357a51cfc1b5579baab3b03c --- /dev/null +++ b/p3/preprocess/Mesothelioma/gene_data/GSE117668.csv @@ -0,0 +1,2038 @@ +Gene,GSM3305861,GSM3305862,GSM3305863,GSM3305864,GSM3305865,GSM3305866,GSM3305867,GSM3305868,GSM3305869,GSM3305870,GSM3305871,GSM3305872,GSM3305873,GSM3305874,GSM3305875,GSM3305876,GSM3305877,GSM3305878,GSM3305879,GSM3305880,GSM3305881,GSM3305882,GSM3305883,GSM3305884,GSM3305885,GSM3305886,GSM3305887,GSM3305888,GSM3305889,GSM3305890,GSM3305891,GSM3305892,GSM3305893,GSM3305894,GSM3305895,GSM3305896,GSM3305897,GSM3305898,GSM3305899,GSM3305900,GSM3305901,GSM3305902,GSM3305903,GSM3305904,GSM3305905,GSM3305906,GSM3305907,GSM3305908 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+ATP2C1,107.285796403,84.829221585,109.18488861,104.222879502,105.33790603,78.926967971,137.035502689,129.744080459,123.93469518,117.299386896,118.846425958,92.25214542,154.221440922,113.53319770899999,89.386357889,86.792209152,114.17039112699999,92.340360712,105.519444858,120.38096703699999,90.411312868,92.516834353,104.222879502,145.837811243,146.561631403,152.622891885,109.81573689700001,101.427901785,113.53319770899999,125.917906563,108.99326767500001,129.482653336,137.99308272300001,134.099263836,109.151190029,133.923841611,119.778126767,103.920009215,100.958644671,117.213323536,147.923842579,115.614564719,136.738284657,88.782460415,141.443311166,88.629342884 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+ATP5F1D,8.454774113,8.582343692,20.48257762,11.41583965,20.15341269,15.24106114,18.38991848,13.04035114,15.05316601,0.0,10.3340084,14.20652365,18.92679078,17.45390472,9.988675172,14.72565264,21.46319152,17.01426237,8.408422306,15.5133132,5.178744951,18.08802212,11.41583965,22.99209841,13.19830998,16.11629836,10.82087119,18.24881352,17.45390472,13.82897583,19.63951618,16.44578633,13.966879,9.816675157,21.45548366,14.49410274,10.07921194,22.50630687,11.42228795,14.83526982,21.13522426,7.727704936,20.20637508,18.46017882,22.77558823,13.34167963 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+CHMP5,7.465266555,8.348665375,12.24523098,6.422754058,8.312781632,7.62230785,10.8472281,10.05156058,10.40351218,6.506082395,8.203596646,6.072802195,10.92627021,7.893752074,7.154973743,8.483783758,9.304735065,7.550270065,13.09287457,8.366773754,8.055558111,7.955910393,6.422754058,14.50451431,11.16342755,9.099580907,8.551878965,9.407584535,7.893752074,7.138427262,6.405198626,7.801571537,8.943021698,8.237967229,8.032892813,6.682463437,8.129345379,9.036077194,9.459251671,8.113669818,11.1237674,10.07818125,11.47061958,9.594049126,8.629828375,7.496139671 +CHMP6,2.425942182,3.445456276,2.668427562,3.478859211,3.123072359,1.974031601,4.106630613,3.265548025,4.54882716,3.431931235,2.408344744,3.622009095,4.87607736,3.227232622,1.672634268,2.984274577,1.828794301,3.164127556,4.370053526,2.950093772,2.379330444,2.89444774,3.478859211,2.105617011,3.371521347,2.544039883,3.350560895,1.591974112,3.227232622,2.817224893,1.903024739,3.722000786,4.595483354,4.344922704,3.860698015,2.641857402,3.682801852,1.821340071,1.905853285,2.807367679,5.231278046,1.552608323,5.501596822,2.56415818,4.550339396,1.95145617 +CHMP7,1.384990718,1.398060702,1.673518305,1.398975458,1.578097477,1.472745992,2.007029628,1.802866322,1.114640489,1.68141776,1.865402748,0.90927592,2.022518663,1.15538716,1.304957871,0.847681941,1.526711128,1.692163761,1.605158281,2.480767892,1.44406319,1.811049252,1.398975458,2.495518109,2.088443228,1.348868129,2.002716521,1.692631617,1.15538716,1.920353864,1.825701963,1.578625884,1.761928471,1.338394174,1.453817706,0.811829739,1.354239388,1.286265807,1.756194439,1.979750046,2.143435548,1.401738213,1.731957207,1.708177755,1.476883921,1.563940589 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+CLINT1,62.878088506,44.455552108,37.039332787999996,55.153316935,41.413630649,53.105957196000006,39.375502274,38.794406337,37.543081101,44.654977964,48.181765324000004,41.003988799999995,44.849545645,48.126333601,47.307823438999996,42.808067584,44.953088293,39.998707902999996,39.187982125,53.769592769,53.971841811,55.464349369,55.153316935,30.467810219,39.569410514,38.817570419,36.16276069,60.302523038000004,48.126333601,48.817255195,60.110456767,39.182897217000004,61.470358074,49.625618048999996,34.388435113,49.342859726,38.571967724000004,50.243420241,57.079248702,47.759170267,34.774066722,49.841596096,39.627032322,48.653807711999995,41.024822946,58.839330616 +CLIP1,12.383763459,13.298958995000001,12.297427579999999,18.670299737,7.0543626249999996,7.024696574,16.638823426,16.835054023,18.499348820999998,17.135911958999998,11.344189643,14.47310291,18.202086140000002,11.274118041,12.573756233000001,11.166739859,7.280159723000001,10.596084302,9.324887845,11.790636641999999,9.544773807999999,11.515189264,18.670299737,13.808628199,22.002751439,27.32916548,12.925236222,19.919036176,11.274118041,10.054127472000001,16.654002633,13.900423404,23.08529499,17.683376697,12.258365556000001,11.763823148,23.582085681,7.559774216000001,9.340953342,14.149451697,8.825226347000001,13.515299173,16.339992502999998,11.110287172,14.366985639,9.142745717 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+CLK1,9.169209036,5.725912598,12.20706426,22.63441046,6.720979308,13.69827944,11.41301069,15.26363623,8.667784548,13.35896234,9.421313382,10.02433205,9.994089021,13.50805756,11.76651833,9.698360825,4.749288417,6.025706857,6.475344002,6.866116758,9.914598921,17.256848,22.63441046,5.94220614,10.86081951,12.73872486,7.580002236,16.44479293,13.50805756,11.03105448,12.91814376,18.34456039,16.17746124,17.30992703,10.45351819,12.09953749,11.80457082,10.29139799,13.32509741,15.80076298,7.848049183,7.498532863,9.859663992,12.65266985,9.982895255,16.25799866 +CLK2P1,0.730464254,0.632411711,0.622440152,0.346510176,0.35902513,0.423576141,0.772438342,0.310364037,0.224593239,0.615709657,0.614385624,0.605501365,0.569535416,0.54581746,0.493865449,0.324268711,0.296186782,1.344903946,0.43768026,0.320449168,0.569943506,0.693779673,0.346510176,0.307991253,0.509426656,0.338588893,0.627939726,0.630832252,0.54581746,0.309209813,0.480489791,0.634197804,0.687915316,0.528708692,0.273877672,0.255087533,0.696861873,0.692721945,0.505503908,0.55179851,0.720952376,0.219795187,0.756912018,0.395457142,0.333274898,0.920872188 +CLK3,6.392564861,5.627055615,14.90653776,10.95412291,8.772227289,8.052387604,11.93679761,13.11474297,12.52458795,9.576514741,6.246238775,5.364809722,12.7104868,11.28462939,4.862217387,6.628450318,13.54881725,7.122000179,9.124928069,9.542758319,7.156483304,9.436749695,10.95412291,17.26282902,11.05238317,9.835768738,10.60626091,9.952438582,11.28462939,8.165010996,7.594534857,7.677217413,8.628936289,11.89467227,13.62732366,9.766331061,9.901269528,14.0403238,8.448580028,8.679489232,13.84236865,6.096235091,13.23380283,8.811939043,9.657683969,6.853929083 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+MOSPD2,3.704005136,3.270064424,2.920113286,4.729461494,1.651293069,1.943564219,5.674903033,3.519675437,2.405103647,4.027317996,2.403625617,3.739584436,7.324191875,3.029744521,2.175444828,1.981573002,1.196747611,3.010135484,1.830930935,1.99277144,2.599671054,2.63439163,4.729461494,1.334328221,2.410251823,3.65018179,2.241295202,5.648023915,3.029744521,2.163480347,3.000500639,2.223468421,4.609068882,5.643283875,2.395686512,2.241477888,3.430238006,0.952334922,2.456274396,2.709300793,1.211165283,2.566757908,1.868160187,2.658050095,2.978120192,1.677059516 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+MOV10,2.820531378,2.483941025,2.785497319,2.163257496,3.767583828,2.801105806,2.832864917,2.14031619,2.396941695,2.484492027,3.655011992,2.443190252,3.286163797,2.958264978,2.676214248,2.432853269,2.260197097,2.324730915,2.792233505,3.005173148,2.569228953,2.900801222,2.163257496,4.372525953,2.971478621,3.720029676,2.507509775,3.983935968,2.958264978,2.5341783,3.265879494,2.490107822,2.887224567,2.666444519,4.188221249,3.510290158,2.463253344,4.01292183,3.549451902,2.107581022,3.205800098,2.206195133,2.737714473,3.158556263,2.38478467,2.833474307 +MOV10L1,0.921701992,0.765421378,0.180251241,0.417651461,0.94645441,1.017725775,1.44211703,0.156061243,0.524431151,0.503287594,0.501498536,0.812087512,0.557238124,0.832447929,0.522342372,0.61181547,0.538161608,0.97774354,0.660714859,0.422270816,0.359312134,0.259164345,0.417651461,0.568852976,0.266094762,0.57557324,0.891684297,0.59227545,0.832447929,0.962070143,0.724626742,1.358709767,0.521132274,0.248317397,0.369881885,0.834758998,1.754619176,0.237050284,0.143244171,0.794131609,0.4254045,1.416536918,0.112176014,0.17078951,0.53052999,0.259007592 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a/p3/preprocess/Metabolic_Rate/cohort_info.json b/p3/preprocess/Metabolic_Rate/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..195092598f93e49aa90f380006cd6e801131360b --- /dev/null +++ b/p3/preprocess/Metabolic_Rate/cohort_info.json @@ -0,0 +1 @@ +{"GSE89231": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 49, "note": "Dataset contains doxorubicin sensitivity data from DLBCL cell lines, suitable for binary analysis."}, "GSE61225": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 74, "note": "Contains swimming pool exposure data with metabolic rate and gene expression measurements"}, "GSE41168": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": 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