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a/p3/preprocess/Bipolar_disorder/clinical_data/GSE93114.csv b/p3/preprocess/Bipolar_disorder/clinical_data/GSE93114.csv new file mode 100644 index 0000000000000000000000000000000000000000..38ba25f33fc3fefe9282a07f61e446c076d29ac7 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/clinical_data/GSE93114.csv @@ -0,0 +1,2 @@ +,GSM2444026,GSM2444027,GSM2444028,GSM2444029,GSM2444030,GSM2444031,GSM2444032,GSM2444033,GSM2444034,GSM2444035,GSM2444036,GSM2444037,GSM2444038,GSM2444039,GSM2444040,GSM2444041 +Bipolar_disorder,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Bipolar_disorder/code/GSE120340.py b/p3/preprocess/Bipolar_disorder/code/GSE120340.py new file mode 100644 index 0000000000000000000000000000000000000000..fe7af57758fcbb0b81694bd17209b745740244fd --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE120340.py @@ -0,0 +1,158 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE120340" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120340" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE120340.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE120340.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE120340.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 +# From background info, this is an "expression microarray" study +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 0 # 'disease state' contains BD/control info +age_row = None # Age not available in sample characteristics +gender_row = None # Gender mentioned as matched but not provided + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + value = x.split(': ')[-1].strip().upper() + if value == 'BD(+)' or value == 'BD(-)': + return 1 # Any BD type maps to case + elif value == 'CONTROL': + return 0 # Control maps to control + elif value == 'SCZ': + return None # Not relevant for bipolar study + return None + +def convert_age(x): + return None # No age data + +def convert_gender(x): + return None # No gender data + +# 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: + selected_clinical_df = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait) + + # Preview data + preview = preview_df(selected_clinical_df) + print("Clinical data preview:", preview) + + # Save clinical features + selected_clinical_df.to_csv(out_clinical_data_file) +# Extract genetic data from the matrix file +try: + genetic_data = get_genetic_data(matrix_file_path) + print("First 20 row IDs:") + print(genetic_data.index[:20]) +except Exception as e: + print(f"Error reading genetic data: {e}") + +# Looking at the row IDs which appear to be gene/transcript identifiers, +# and considering the background info mentioning "expression microarrays", +# maintain original assessment that this is gene expression data +is_gene_available = True + +# Save metadata with correct 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) +) +# The ids contain "_at" suffix which is characteristic of Affymetrix probe IDs +# These need to be mapped to gene symbols for consistent 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) +# 1. Description column contains gene names +prob_col = 'ID' +gene_col = 'Description' + +# 2. Get mapping between probe IDs and gene names +mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# 3. Apply mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview results +print("\nFirst few rows and columns of mapped gene expression data:") +print(gene_data.iloc[:5, :5]) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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="Expression microarray data from post-mortem brain samples (BA46) of bipolar disorder patients and controls." +) + +# 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/Bipolar_disorder/code/GSE120342.py b/p3/preprocess/Bipolar_disorder/code/GSE120342.py new file mode 100644 index 0000000000000000000000000000000000000000..b5c38859deddd271d504dd88f237239204b98f73 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE120342.py @@ -0,0 +1,114 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE120342" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120342" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE120342.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE120342.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE120342.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 mentioning "transcriptomes", this dataset likely contains gene expression data +is_gene_available = True + +# 2. Variable Analysis +# 2.1 Data Availability +# Trait (BD status) is available in index 0 +trait_row = 0 +# Age data is not available +age_row = None +# Gender data is not available +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + if not isinstance(value, str): + return None + value = value.lower().strip() + if 'disease state:' in value: + value = value.split('disease state:')[1].strip() + if value == 'bd(+)' or value == 'bd(-)': # Both BD+ and BD- are cases + return 1 + elif value == 'control': + return 0 + return None + +def convert_age(value): + return None # Not available + +def convert_gender(value): + return None # Not available + +# 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=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + preview = preview_df(clinical_features) + print("Preview of processed clinical features:") + print(preview) + + # Save to CSV + clinical_features.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/Bipolar_disorder/code/GSE45484.py b/p3/preprocess/Bipolar_disorder/code/GSE45484.py new file mode 100644 index 0000000000000000000000000000000000000000..558a61b925e34d45b7850ba00bae73496dff94fa --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE45484.py @@ -0,0 +1,167 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE45484" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE45484" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE45484.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE45484.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE45484.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 # Based on series description, this is a gene expression study from blood samples + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 2 # 'responder' indicates treatment response status +age_row = 4 # Contains age information +gender_row = 3 # Contains sex information + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert treatment response to binary (0: non-responder, 1: responder)""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().upper() + if value == 'NO': + return 0 + elif value == 'YES': + return 1 + return None + +def convert_age(value: str) -> float: + """Convert age to continuous numeric value""" + if not value or ':' not in value: + return None + try: + return float(value.split(':')[1].strip()) + except: + return None + +def convert_gender(value: str) -> int: + """Convert gender to binary (0: female, 1: male)""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().upper() + if value == 'F': + return 0 + elif value == '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: + selected_clinical_df = geo_select_clinical_features( + clinical_data, + trait="Treatment_Response", + 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 data + preview = preview_df(selected_clinical_df) + print("Preview of processed clinical data:") + print(preview) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# The gene identifiers are ILMN_ probe IDs used in Illumina microarrays +# These need to be mapped to gene symbols +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 +prob_col = 'ID' # Column containing probe IDs (ILMN_*) +gene_col = 'Symbol' # Column containing gene symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# Apply mapping to convert probe expressions to gene expressions +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the mapped data +print("First few genes and their expression values:") +preview = preview_df(gene_data) +print(preview) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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, "Treatment_Response") + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "Treatment_Response") + +# 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 blood samples of bipolar disorder patients receiving lithium treatment 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/Bipolar_disorder/code/GSE46416.py b/p3/preprocess/Bipolar_disorder/code/GSE46416.py new file mode 100644 index 0000000000000000000000000000000000000000..61a5b387312c63bdcf23aee4609af31395870f0c --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE46416.py @@ -0,0 +1,267 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE46416" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46416" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE46416.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE46416.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE46416.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 +# From the series title and summary, this study analyzed gene expression profiles +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Trait (bipolar disorder) can be derived from disease status and bd phase +trait_row = 1 +# Age and gender not available in sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion +def convert_trait(value): + """Convert disease status to binary: control=0, BD=1""" + if pd.isna(value): + return None + value = value.split(": ")[1].lower() + if "control" in value: + return 0 + elif "bipolar disorder" in value or "bd" in value: + return 1 + return None + +def convert_age(value): + return None # Not used since age not available + +def convert_gender(value): + return None # Not used since gender 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 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 extracted clinical data +print("Preview of clinical data:") +print(preview_df(selected_clinical)) + +# Save clinical data +selected_clinical.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# The gene identifiers are numeric codes, not standard human gene symbols +# These appear to be probe IDs that will need to be mapped to gene symbols +requires_gene_mapping = True +# Extract gene annotation data with common GEO gene annotation prefixes +gene_metadata = get_gene_annotation(soft_file_path, prefixes=['ID_REF', 'GENE', 'GENE_SYMBOL', 'Symbol']) + +# Preview the gene metadata to verify we got the annotation data +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Let's properly extract platform annotation data from the SOFT file +with gzip.open(soft_file_path, 'rt') as file: + platform_section = False + platform_data = [] + for line in file: + if '!Platform_table_begin' in line: + platform_section = True + # Skip the header line + next(file) + continue + if '!Platform_table_end' in line: + platform_section = False + continue + if platform_section and line.strip(): + platform_data.append(line.strip().split('\t')) + +# Convert to DataFrame +gene_metadata = pd.DataFrame(platform_data[1:], columns=platform_data[0]) +print("\nPlatform annotation columns:") +print(gene_metadata.columns.tolist()) +print("\nFirst few rows:") +print(gene_metadata.head()) + +# Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol') + +# Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview mapped gene data +print("\nPreview of gene expression data after mapping:") +preview_subset = gene_data.iloc[:5, :5] +print(preview_subset) +# First read and print some content of the SOFT file to understand its structure +print("Previewing SOFT file structure:") +with gzip.open(soft_file_path, 'rt') as file: + platform_data = [] + in_table = False + header = None + + # Read through file line by line + for line in file: + line = line.strip() + + # Start collecting data when we reach the platform table + if line.startswith('^PLATFORM'): + in_table = True + continue + + # Only process lines when we're in the platform section + if in_table: + # Skip empty lines and comment lines + if not line or line.startswith('!') or line.startswith('#'): + continue + + # First non-comment line is the header + if header is None: + header = line.split('\t') + continue + + # Add data lines to our collection + platform_data.append(line.split('\t')) + + # Stop when we reach the end of platform section + if line.startswith('^SERIES'): + break + +# Convert to DataFrame +gene_metadata = pd.DataFrame(platform_data, columns=header) + +print("\nPlatform annotation columns:") +print(gene_metadata.columns.tolist()) + +print("\nFirst few rows:") +print(gene_metadata.head()) + +# Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID_REF', gene_col='Gene Symbol') + +# Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +print("\nPreview of mapped gene data:") +print(gene_data.iloc[:5, :5]) + +# Save gene data +gene_data.to_csv(out_gene_data_file) +# First read and extract platform annotation data +platform_data = [] +header = None +with gzip.open(soft_file_path, 'rt') as file: + in_platform = False + for line in file: + line = line.strip() + if line.startswith('!platform_table_begin'): + in_platform = True + continue + elif line.startswith('!platform_table_end'): + break + elif in_platform: + if header is None: + header = line.split('\t') + else: + fields = line.split('\t') + if len(fields) == len(header): # Only add rows matching header length + platform_data.append(fields) + +if header is None: + raise ValueError("Could not find platform annotation data in SOFT file") + +# Convert to DataFrame +gene_metadata = pd.DataFrame(platform_data, columns=header) + +# Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol') + +# Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 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) + +# 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 blood samples comparing bipolar disorder patients in manic vs euthymic phases with controls." +) + +# 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) +# 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) \ No newline at end of file diff --git a/p3/preprocess/Bipolar_disorder/code/GSE46449.py b/p3/preprocess/Bipolar_disorder/code/GSE46449.py new file mode 100644 index 0000000000000000000000000000000000000000..a00195740d16dfe873143e3ff699df27b23ff82a --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE46449.py @@ -0,0 +1,157 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE46449" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46449" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE46449.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE46449.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE46449.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 +# The dataset uses Affymetrix microarrays for gene expression, so gene data is available +is_gene_available = True + +# 2.1 Data Row Identification +trait_row = 1 # 'genotype' contains trait information +age_row = 2 # 'age' data is available +gender_row = 3 # 'gender' data is available but shows only male + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + x = x.lower().split(': ')[-1] + if 'bipolar' in x: + return 1 + elif 'control' in x: + return 0 + return None + +def convert_age(x): + if not isinstance(x, str): + return None + try: + return float(x.split(': ')[-1]) + except: + return None + +def convert_gender(x): + if not isinstance(x, str): + return None + x = x.lower().split(': ')[-1] + if x == 'male': + return 1 + elif x == 'female': + return 0 + 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: + 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 data + preview = preview_df(selected_clinical_df) + print("Preview of selected clinical features:") + print(preview) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These appear to be probe IDs from Affymetrix arrays (e.g. "1007_s_at" format) +# They need to be mapped to human gene symbols for 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) +# Get gene mapping between probe IDs and gene symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the gene expression data +print("Preview of mapped gene expression data:") +preview_subset = gene_data.iloc[:5, :5] +print(preview_subset) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 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/Bipolar_disorder/code/GSE53987.py b/p3/preprocess/Bipolar_disorder/code/GSE53987.py new file mode 100644 index 0000000000000000000000000000000000000000..e09bd7bcc9f4f31f585c2e04e30b0224148a62d3 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE53987.py @@ -0,0 +1,161 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE53987" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE53987" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE53987.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE53987.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE53987.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 +# The background info states "hybridized to U133_Plus2 Affymetrix chips" which is a gene expression microarray +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Disease state (trait) is in row 7 +trait_row = 7 +# Age is in row 0 +age_row = 0 +# Gender is in row 1 +gender_row = 1 + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + # Extract value after colon and strip whitespace + value = value.split(":")[-1].strip() + # Map disease states to binary (control=0, bipolar=1) + if value == "control": + return 0 + elif value == "bipolar disorder": + return 1 + # Skip other disease states + return None + +def convert_age(value): + # Extract numeric age value + try: + age = float(value.split(":")[-1].strip()) + return age + except: + return None + +def convert_gender(value): + # Extract gender value and convert to binary + value = value.split(":")[-1].strip() + if value == "F": + return 0 + elif value == "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=trait_row is not None) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = 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 processed clinical data + preview = preview_df(clinical_features) + print("Preview of clinical features:") + print(preview) + + # Save clinical data + clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These appear to be Affymetrix probe IDs (####_at format) rather than gene symbols +# They will need to be mapped to official human gene symbols for 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) +# Get mapping between probe IDs and gene symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview gene data after mapping +print("Data preview after mapping to gene symbols:") +preview_subset = gene_data.iloc[:5, :5] +print(preview_subset) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 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/Bipolar_disorder/code/GSE62191.py b/p3/preprocess/Bipolar_disorder/code/GSE62191.py new file mode 100644 index 0000000000000000000000000000000000000000..9eeb7d6e927131137889f27e2c69a26ed8715677 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE62191.py @@ -0,0 +1,158 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE62191" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE62191" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE62191.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE62191.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE62191.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 gene expression study according to title and summary +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Row identification +trait_row = 1 # Disease state is recorded in row 1 +age_row = 2 # Age is recorded in row 2 +gender_row = 6 # Gender is recorded in row 6 + +# 2.2 Conversion functions +def convert_trait(x): + if pd.isna(x): + return None + value = x.split(": ")[1].lower() + if "bipolar disorder" in value: + return 1 + elif "healthy control" in value: + return 0 + return None + +def convert_age(x): + if pd.isna(x): + return None + try: + return int(x.split(": ")[1].split(" ")[0]) + except: + return None + +def convert_gender(x): + if pd.isna(x): + return None + value = x.split(": ")[1].lower() + if value == "male": + return 1 + elif value == "female": + return 0 + return None + +# 3. Save initial validation result +is_validated = 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 +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 +preview_result = preview_df(selected_clinical) + +# Save clinical data +selected_clinical.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# The identifiers appear to be numeric probe IDs rather than human gene symbols +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 probe_id and gene_symbol mapping from annotation data +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL') + +# Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the gene expression data +print("\nGene expression data preview:") +preview_subset = gene_data.iloc[:5, :5] +print(preview_subset) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 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/Bipolar_disorder/code/GSE67311.py b/p3/preprocess/Bipolar_disorder/code/GSE67311.py new file mode 100644 index 0000000000000000000000000000000000000000..58147e59a5e1c72fc1f22a67b9c31992ffa20947 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE67311.py @@ -0,0 +1,154 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE67311" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE67311" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE67311.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE67311.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE67311.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 this is a gene expression dataset using Affymetrix® Human Gene 1.1 ST arrays +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# Trait (Bipolar disorder) is recorded in row 7 +trait_row = 7 + +def convert_trait(x): + if pd.isna(x): + return None + value = x.split(': ')[1] + if value == 'Yes': + return 1 + elif value == 'No': + return 0 + return None + +# Age is not available +age_row = None +convert_age = None + +# Gender is not available +gender_row = None +convert_gender = None + +# 3. Save initial filtering result +note = "Dataset is primarily focused on fibromyalgia with bipolar disorder only recorded as comorbidity. Very few bipolar cases (~2/140) make it unsuitable for bipolar disorder research." + +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), + note=note +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_df = 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 extracted features + preview = preview_df(clinical_df) + print("Preview of clinical features:", preview) + + # Save to CSV + clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These numeric identifiers appear to be microarray probe IDs, not gene symbols +# They are likely Illumina or Affymetrix probe IDs that need to be mapped to gene symbols +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) +# Get gene mapping from annotation data +# 'ID' column contains probe IDs matching gene expression data +# Extract gene symbols directly from gene_assignment field using extract_human_gene_symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') + +# Convert probe-level measurements to gene expression data using the library's function +# The apply_gene_mapping function already includes the extract_human_gene_symbols logic +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview results +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows and columns of gene expression data:") +print(gene_data.iloc[:5, :5]) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 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/Bipolar_disorder/code/GSE92538.py b/p3/preprocess/Bipolar_disorder/code/GSE92538.py new file mode 100644 index 0000000000000000000000000000000000000000..9d32b6c3d34c40d7356680baf8458f3a278a1686 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE92538.py @@ -0,0 +1,168 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE92538" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE92538" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE92538.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE92538.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE92538.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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 dataset contains gene expression data from Affymetrix microarrays +is_gene_available = True + +# 2.1 Data Availability +# Trait (Bipolar Disorder) data is in row 2 under 'diagnosis' +trait_row = 2 + +# Age data is in row 8 +age_row = 8 + +# Gender data is in row 6 +gender_row = 6 + +# 2.2 Data Type Conversion Functions +def convert_trait(x: str) -> Optional[int]: + """Convert diagnosis to binary indicating bipolar disorder""" + if not x or 'diagnosis:' not in x: + return None + val = x.split('diagnosis:')[1].strip() + if 'Bipolar Disorder' in val: + return 1 + elif val in ['Control', 'Schizophrenia', 'Major Depressive Disorder']: + return 0 + return None + +def convert_age(x: str) -> Optional[float]: + """Convert age to continuous value""" + if not x or 'age:' not in x: + return None + try: + return float(x.split('age:')[1].strip()) + except: + return None + +def convert_gender(x: str) -> Optional[int]: + """Convert gender to binary (0=female, 1=male)""" + if not x or 'gender:' not in x: + return None + val = x.split('gender:')[1].strip() + 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=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Extract clinical features using helper function +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 extracted clinical features:") +print(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) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These IDs are in Affymetrix ID format (_at suffix), not gene symbols +# They need to be mapped to official gene symbols +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) +# 1. Identify relevant columns: 'ID' contains probe IDs and 'SYMBOL' contains gene symbols +prob_col = 'ID' +gene_col = 'SYMBOL' + +# 2. Get gene mapping dataframe +mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# 3. Apply gene mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 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/Bipolar_disorder/code/GSE93114.py b/p3/preprocess/Bipolar_disorder/code/GSE93114.py new file mode 100644 index 0000000000000000000000000000000000000000..52c9d441dc01db6f66debe03dd9d0853e3cd42fc --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/GSE93114.py @@ -0,0 +1,94 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" +cohort = "GSE93114" + +# Input paths +in_trait_dir = "../DATA/GEO/Bipolar_disorder" +in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE93114" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/GSE93114.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/GSE93114.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/GSE93114.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/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. Check gene expression data availability +is_gene_available = True # Series title mentions "Gene expression data" + +# 2.1 Data Availability +# All samples are bipolar disorder cases - no controls, so trait data not usable +trait_row = None # No trait variability +age_row = None # Age not available +gender_row = None # Gender not available + +# 2.2 Convert functions +def convert_trait(val: str) -> int: + """Convert bipolar disorder status to binary value""" + if not isinstance(val, str): + return None + val = val.lower() + if 'bipolar disorder' in val: + return 1 + elif 'control' in val: + return 0 + return None + +def convert_age(val: str) -> float: + """Convert age to float - not used since age not available""" + return None + +def convert_gender(val: str) -> int: + """Convert gender to binary - not used since gender not available""" + 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. Skip clinical feature extraction since trait_row is None +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These identifiers appear to be probe IDs from a microarray platform, not human gene symbols +# They need to be mapped to standard gene symbols for analysis +requires_gene_mapping = True +# Update metadata with correct gene availability assessment +is_gene_available = False # Dataset contains miRNA, not gene expression data +_ = 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) +) \ No newline at end of file diff --git a/p3/preprocess/Bipolar_disorder/code/TCGA.py b/p3/preprocess/Bipolar_disorder/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..d3d64af5ebd8bc400b6f6938dd5c56b2b6e9251b --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/code/TCGA.py @@ -0,0 +1,31 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bipolar_disorder" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Bipolar_disorder/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Bipolar_disorder/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Bipolar_disorder/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Bipolar_disorder/cohort_info.json" + +# Check if directory exists for bipolar disorder +directories = os.listdir(tcga_root_dir) +relevant_dirs = [d for d in directories if not d.startswith('.') and os.path.isdir(os.path.join(tcga_root_dir, d))] + +# No relevant cohort exists for bipolar disorder in TCGA (cancer database) +is_gene_available = False +is_trait_available = False + +# Record this information and exit pipeline +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 +) \ No newline at end of file diff --git a/p3/preprocess/Bipolar_disorder/gene_data/GSE120340.csv b/p3/preprocess/Bipolar_disorder/gene_data/GSE120340.csv new file mode 100644 index 0000000000000000000000000000000000000000..2317ef5f58ed66bce71f61980c0ec0b5123d554b --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/gene_data/GSE120340.csv @@ -0,0 +1,1172 @@ +Gene,GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506 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+Bladder_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Bladder_Cancer/clinical_data/GSE253531.csv b/p3/preprocess/Bladder_Cancer/clinical_data/GSE253531.csv new file mode 100644 index 0000000000000000000000000000000000000000..2a2623d9e98b52e9237d6558fb598533b3b70ec4 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/clinical_data/GSE253531.csv @@ -0,0 +1,2 @@ +,GSM8022612,GSM8022613,GSM8022614,GSM8022615,GSM8022616,GSM8022617,GSM8022618,GSM8022619,GSM8022620,GSM8022621,GSM8022622,GSM8022623,GSM8022624,GSM8022625,GSM8022626,GSM8022627,GSM8022628,GSM8022629,GSM8022630,GSM8022631,GSM8022632,GSM8022633,GSM8022634,GSM8022635,GSM8022636,GSM8022637,GSM8022638,GSM8022639,GSM8022640,GSM8022641,GSM8022642,GSM8022643,GSM8022644,GSM8022645,GSM8022646,GSM8022647,GSM8022648,GSM8022649,GSM8022650,GSM8022651,GSM8022652,GSM8022653,GSM8022654,GSM8022655,GSM8022656,GSM8022657,GSM8022658,GSM8022659,GSM8022660,GSM8022661,GSM8022662,GSM8022663,GSM8022664,GSM8022665 +Bladder_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Bladder_Cancer/code/GSE138118.py b/p3/preprocess/Bladder_Cancer/code/GSE138118.py new file mode 100644 index 0000000000000000000000000000000000000000..a0647f45b5ddccb32aaa38898fb1ea478983b67a --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE138118.py @@ -0,0 +1,179 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE138118" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE138118" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE138118.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE138118.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE138118.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +is_gene_available = True # Based on the series title and summary, this is gene expression data from blood + +# 2.1 Data Availability +trait_row = 0 # "stage at sample" contains cancer status +age_row = 1 # Age information is available +gender_row = None # Gender information is not available in the characteristics + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + value = x.split(': ')[-1].strip() + # Convert to binary: Healthy=0, Any cancer stage=1 + if value == 'Healthy': + return 0 + elif value in ['G1', 'G2', 'G3', 'G1 pTa', 'G2 pTa']: + return 1 + return None + +def convert_age(x): + if pd.isna(x): + return None + value = x.split(': ')[-1].strip() + try: + # Convert to continuous numeric value + return float(value) + except: + return None + +def convert_gender(x): + return None # Not used since gender data is not available + +# 3. Save Initial Filtering Results +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 +) + +# Preview the processed clinical data +preview_result = preview_df(clinical_df) +print("Preview of processed clinical data:") +print(preview_result) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Observe the gene identifiers +# The identifiers are numeric strings starting with '16650', which appears to be probe IDs +# These are not standard human gene symbols and will need to be mapped +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# Get gene mapping data from annotation +# For this dataset: +# ID column contains probe IDs matching gene expression data +# gene_assignment column contains gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') + +# Apply gene mapping to convert probe expression to gene expression +# The apply_gene_mapping function handles many-to-many mappings +gene_data = apply_gene_mapping(gene_data, mapping_df) + +# Preview data shape and first few rows +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# Load previously saved clinical data +clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +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="NanoString nCounter RNA profiling data for bladder cancer recurrence study" +) + +# 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/Bladder_Cancer/code/GSE145261.py b/p3/preprocess/Bladder_Cancer/code/GSE145261.py new file mode 100644 index 0000000000000000000000000000000000000000..50e4b5c48038b0b968d5d205e4c602af5f68bcbc --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE145261.py @@ -0,0 +1,201 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE145261" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE145261" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE145261.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE145261.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE145261.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +is_gene_available = True # Based on context, this dataset studies carcinoma with molecular analysis + +# 2. Variable Availability and Data Type Conversion +# 2.1 Row identification +trait_row = 3 # "tissue type" contains SCC vs UC info +age_row = 0 # "subject age" contains age info +gender_row = 1 # "subject gender" contains gender info + +# 2.2 Conversion functions +def convert_trait(x): + # Binary: SCC (1) vs UC (0) + if not isinstance(x, str): + return None + x = x.lower() + if 'scc' in x or 'small cell' in x: + return 1 + elif 'uc' in x or 'urothelial' in x: + return 0 + return None + +def convert_age(x): + # Continuous: extract age in years + if not isinstance(x, str): + return None + try: + age = int(''.join(filter(str.isdigit, x))) + if 0 <= age <= 120: # Basic age validation + return age + return None + except: + return None + +def convert_gender(x): + # Binary: female (0) vs male (1) + if not isinstance(x, str): + return None + x = x.lower() + if 'female' in x: + return 0 + elif 'male' in x: + 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. 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 extracted features + preview_data = preview_df(selected_clinical_df) + print("Preview of extracted clinical features:", preview_data) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# These are Illumina probes (starting with ILMN_), not gene symbols +# We'll need to map them to human gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation from SOFT file using default prefixes +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation shape:", gene_annotation.shape) +print("\nGene annotation columns and first few values:") +print(preview_df(gene_annotation)) + +# Also inspect the raw SOFT file annotation section to verify parsing +import gzip +with gzip.open(soft_file, 'rt') as f: + found_table = False + lines = [] + for line in f: + if '!platform_table_begin' in line.lower(): + found_table = True + lines.append(next(f)) # Get header line + for _ in range(3): # Get first 3 data lines + lines.append(next(f)) + break + +if found_table: + print("\nRaw annotation format in SOFT file:") + for line in lines: + print(line.strip()) +# Extract gene mapping from annotation +# 'ID' column contains probe IDs (ILMN_*) matching the gene expression data +# 'Symbol' column contains gene symbols we want to map to +gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') + +# Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(gene_data, gene_mapping) + +# Print info about the conversion +print("Shape of mapped gene expression data:", gene_data.shape) +print("\nFirst few rows of mapped gene expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_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 +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +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="NanoString nCounter RNA profiling data for bladder cancer recurrence study" +) + +# 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/Bladder_Cancer/code/GSE162253.py b/p3/preprocess/Bladder_Cancer/code/GSE162253.py new file mode 100644 index 0000000000000000000000000000000000000000..0fc259d12939a5f31e9e82ecd0f900b7140fe296 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE162253.py @@ -0,0 +1,205 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE162253" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE162253" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE162253.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE162253.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE162253.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data +is_gene_available = True # From Series title and samples, this appears to be gene expression data + +# 2. Variable Availability and Data Type Conversion +trait_row = 1 # Feature 1 shows infection status +age_row = None # Age not available +gender_row = None # Gender not available + +def convert_trait(value: str) -> int: + """Convert infection status to binary (0 for control, 1 for infected)""" + if pd.isna(value) or not isinstance(value, str): + return None + value = value.split(': ')[-1].lower() + if 'none' in value or 'control' in value: + return 0 + elif 'ecoli' in value or 'e. coli' in value: + return 1 + return None + +def convert_age(value: str) -> float: + return None + +def convert_gender(value: str) -> int: + 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_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait) + preview_result = preview_df(clinical_df) + print("Preview of clinical data:") + print(preview_result) + clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Based on the gene identifiers like "11715100_at", these appear to be Affymetrix probe IDs +# rather than standard human gene symbols. They will need to be mapped to gene symbols. +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# 1. Load SOFT file again and inspect data more carefully +with gzip.open(soft_file, 'rt') as f: + first_lines = [] + for i, line in enumerate(f): + if i < 100: + first_lines.append(line.strip()) + else: + break + +print("\nFirst few lines of SOFT file to check correct column names:") +for line in first_lines[:10]: + print(line) + +# Re-extract annotation with more careful column selection +gene_annotation = get_gene_annotation(soft_file) + +# 2. Get gene mapping dataframe using correct columns +prob_col = 'ID' +gene_col = 'Gene Symbol' + +# Print columns to verify +print("\nAnnotation columns:", gene_annotation.columns.tolist()) + +# Get probe-gene mapping +mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col) + +print("\nFirst few rows of mapping data:") +print(mapping_data.head()) + +# 3. Apply gene mapping +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# Preview results +print("\nShape of mapped gene expression data:", gene_data.shape) +print("\nFirst few rows of mapped gene data:") +print(gene_data.head()) +if gene_data.empty: + print("Gene expression data is empty after mapping. Checking probe ID formats...") + print("\nSample gene expression probe IDs:") + print(list(gene_data.index.values)[:5]) + print("\nSample annotation probe IDs:") + print(mapping_data['ID'].head().tolist()) + + # Re-run gene mapping with adjusted probe IDs + gene_data = get_genetic_data(matrix_file) + # Convert probe IDs to match annotation format + gene_data.index = gene_data.index.str.replace('_at', '_PM_at').str.replace('_a_at', '_PM_a_at') + + # Apply mapping again + gene_data = apply_gene_mapping(gene_data, mapping_data) + print("\nShape of remapped gene expression data:", gene_data.shape) + +if gene_data.empty: + print("Gene mapping still failed after ID adjustment") + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=False, + is_trait_available=True, + is_biased=None, + df=None, + note="Gene mapping failed - probe ID mismatch between expression and annotation data" + ) +else: + # 1. Normalize gene symbols and save normalized gene data + gene_data.index = gene_data.index.str.replace('-mRNA', '') + gene_data = normalize_gene_symbols_in_index(gene_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_df = pd.read_csv(out_clinical_data_file, index_col=0) + linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) + + # 3. Handle missing values + linked_data = handle_missing_values(linked_data, trait) + + # 4. Check for biased features and remove them if needed + is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # 5. Validate and save cohort info + 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="Mouse gene expression data for bacterial infection study" + ) + + # 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/Bladder_Cancer/code/GSE185264.py b/p3/preprocess/Bladder_Cancer/code/GSE185264.py new file mode 100644 index 0000000000000000000000000000000000000000..a69b968e7c4bb8f3c2f64be431e9a99fab4d6f22 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE185264.py @@ -0,0 +1,153 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE185264" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE185264" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE185264.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE185264.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE185264.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes - RNA quantification data from NanoString nCounter platform +is_gene_available = True + +# 2. Variable Availability and Row Numbers +trait_row = 12 # recurrence data available +age_row = None # age not available +gender_row = 7 # gender data available + +# 2.2 Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert recurrence status to binary (0: no recurrence, 1: recurrence)""" + if pd.isna(value) or value == "NA": + return None + value = value.split(": ")[-1] + if value == "No.Recurrence": + return 0 + elif value == "Recurrence": + return 1 + return None + +def convert_age(value: str) -> Optional[float]: + """Convert age to float - not used since age data unavailable""" + return None + +def convert_gender(value: str) -> Optional[int]: + """Convert gender to binary (0: female, 1: male)""" + if pd.isna(value): + return None + value = value.split(": ")[-1] + if value == "F": + return 0 + elif value == "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. Extract Clinical Features +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 selected features +print(preview_df(selected_clinical_df)) + +# Save clinical data +selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +requires_gene_mapping = False +# 1. Normalize gene symbols and save normalized gene data +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_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 +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +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="NanoString nCounter RNA profiling data for bladder cancer recurrence study" +) + +# 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/Bladder_Cancer/code/GSE201395.py b/p3/preprocess/Bladder_Cancer/code/GSE201395.py new file mode 100644 index 0000000000000000000000000000000000000000..6e4b4e5e610e4fa7575d30e2f1fdbd949638522e --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE201395.py @@ -0,0 +1,140 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE201395" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE201395" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE201395.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE201395.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE201395.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# This dataset uses Affymetrix HTA 2.0 platform for gene expression profiling +is_gene_available = True + +# 2.1 Data Availability +trait_row = None # All are cancer cell lines, no disease status comparison +age_row = None # Age not relevant for cell lines +gender_row = None # Gender not relevant for cell lines + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + return None # Not applicable as all samples are cancer cell lines + +def convert_age(x): + return None # Not applicable for cell lines + +def convert_gender(x): + return None # Not applicable for cell lines + +# 3. Save metadata +is_initial_check = 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. Skip clinical feature extraction since trait_row is None +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# The IDs end with "_st" which indicates these are Affymetrix probeset IDs +# They need to be mapped to standard human gene symbols +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# Extract probe-gene mapping from annotation data +prob_col = 'probeset_id' +gene_col = 'gene_assignment' + +# First extract columns and rename +gene_mapping = gene_annotation[[prob_col, gene_col]] +gene_mapping = gene_mapping.rename(columns={prob_col: 'ID', gene_col: 'Gene'}) +gene_mapping = gene_mapping.dropna() +gene_mapping = gene_mapping.astype({'ID': 'str'}) + +# Apply the gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(gene_data, gene_mapping) + +# Normalize gene symbols using NCBI synonym data +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Save processed gene data +gene_data.to_csv(out_gene_data_file) + +# Print shape and preview of processed data +print("Shape of processed gene expression data:", gene_data.shape) +print("\nFirst few rows of processed data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2-6. Skip clinical linking since no trait data available +is_biased = True # No trait data means biased by definition +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=is_biased, + df=gene_data.T, + note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis." +) \ No newline at end of file diff --git a/p3/preprocess/Bladder_Cancer/code/GSE203149.py b/p3/preprocess/Bladder_Cancer/code/GSE203149.py new file mode 100644 index 0000000000000000000000000000000000000000..c1dba13d913afa12eb69c012b7e29f5f1069671a --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE203149.py @@ -0,0 +1,183 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE203149" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE203149" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE203149.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE203149.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE203149.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes - the background info indicates gene expression data from microarray platform +is_gene_available = True + +# 2.1 Data Availability +# trait_row = 0 - Disease status shown in row 0, but only one value (all samples are cancer) +# age - not available in sample characteristics +# gender - not available in sample characteristics +trait_row = None # Only one value, so not usable +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + value = str(x).split(': ')[-1].lower() + if 'bladder cancer' in value: + return 1 + elif 'control' in value or 'normal' in value: + return 0 + return None + +def convert_age(x): + if pd.isna(x): + return None + value = str(x).split(': ')[-1] + try: + return float(value) + except: + return None + +def convert_gender(x): + if pd.isna(x): + return None + value = str(x).split(': ')[-1].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 +# trait_row is None, so trait data not available +is_trait_available = False if trait_row is None else True + +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. Skip clinical feature extraction since trait_row is None +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# Extract gene identifiers and gene symbols and create mapping +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1') + +# Apply gene mapping to convert probe measurements to gene expression data +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# Normalize gene symbols in the gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Save preprocessed gene expression 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 normalized gene data +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Create minimal clinical data (just sample IDs) and link with gene data +sample_ids = pd.DataFrame(index=gene_data.columns) +linked_data = pd.concat([sample_ids, gene_data.T], axis=1) + +# 3. Handle missing values systematically +# Since we only have gene data, we only need to handle missing gene values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features +is_biased = True # No trait data means the dataset is biased by definition + +# 5. Validate data quality 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=False, + is_biased=is_biased, + df=linked_data, + note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis." +) + +# 6. Since is_biased is True, the data is not usable, so we don't save it +# 1. Normalize gene symbols and save normalized gene data +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2-6. Skip clinical linking since no trait data available +is_biased = True # No trait data means biased by definition +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=is_biased, + df=gene_data.T, + note="All samples are cancer cases (no controls), making trait data unavailable for associative analysis." +) \ No newline at end of file diff --git a/p3/preprocess/Bladder_Cancer/code/GSE222073.py b/p3/preprocess/Bladder_Cancer/code/GSE222073.py new file mode 100644 index 0000000000000000000000000000000000000000..1041e1bb72988bbfbcac271e338e9de5f83c71fb --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE222073.py @@ -0,0 +1,218 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE222073" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE222073" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE222073.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE222073.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE222073.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this appears to be a gene expression dataset based on mentions of RNA subtypes and labeling kits +is_gene_available = True + +# 2.1 Data Availability + +# Trait: Available from various metastasis indicators (rm-* fields) +# Will use rm-bone as representative of metastasis +trait_row = 11 + +# 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 bone metastasis status to binary (0: no, 1: yes)""" + if not isinstance(value, str): + return None + value = value.lower() + if 'rm-bone:' not in value: + return None + value = value.split('rm-bone:')[1].strip() + if value == 'yes': + return 1 + elif value == 'no': + return 0 + return None + +def convert_age(value: str) -> float: + """Placeholder for age conversion""" + return None + +def convert_gender(value: str) -> int: + """Placeholder for gender conversion""" + 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. 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, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + + # Preview the data + preview = preview_df(selected_clinical_df) + print("Preview of clinical data:") + print(preview) + + # Save clinical data + selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Looking at the gene identifiers, we see a mix of symbols like 'A2M', 'A4GALT' which are human gene symbols, +# and some numbered identifiers like '1-Mar', '2-Mar' etc. +# The presence of 'Mar' suggests these might be month-related probe identifiers that need mapping. +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# First locate the platform table section in SOFT file +import gzip +table_begin = None +table_end = None +with gzip.open(soft_file, 'rt', encoding='utf-8') as f: + for i, line in enumerate(f): + if '!platform_table_begin' in line.lower(): + table_begin = i + elif '!platform_table_end' in line.lower() and table_begin is not None: + table_end = i + break + +# Read annotation data between markers using skiprows and nrows +import pandas as pd +if table_begin is not None and table_end is not None: + gene_annotation = pd.read_csv(soft_file, compression='gzip', skiprows=table_begin+1, + nrows=table_end-table_begin-1, sep='\t') +else: + # Fallback to original method if markers not found + gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation preview:") +print(preview_df(gene_annotation)) + +# Display all column names +print("\nAll column names in annotation data:") +print(gene_annotation.columns.tolist()) +# In this dataset, the probe ID "A2M" in the gene expression data should be matched to the gene symbol in 'ORF' column +gene_mapping = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='ORF') + +# Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping) + +# Save the gene expression data +gene_data.to_csv(out_gene_data_file) + +# Preview results +print("Shape of mapped gene expression data:", gene_data.shape) +print("\nFirst few rows of mapped data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +# Remove "-mRNA" suffix from gene symbols before normalization +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data and trait +# First get selected clinical features using the extraction function from previous step +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 +) + +# Debug data structures before linking +print("\nPre-linking data shapes:") +print("Clinical data shape:", selected_clinical.shape) +print("Gene data shape:", gene_data.shape) +print("\nClinical data preview:") +print(selected_clinical.head()) + +# Transpose gene data to match clinical data orientation +gene_data_t = gene_data.T +linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate data quality 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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)." +) + +# 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/Bladder_Cancer/code/GSE244266.py b/p3/preprocess/Bladder_Cancer/code/GSE244266.py new file mode 100644 index 0000000000000000000000000000000000000000..24d36e266270cf54e3bb1ac45a24f4cf72b16852 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE244266.py @@ -0,0 +1,177 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE244266" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE244266" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE244266.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE244266.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE244266.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# The series title and summary suggest this is RNA/gene expression data for molecular subtypes +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 2 # clinical_stage_strat_factor can be used as binary trait +age_row = None # Age not available +gender_row = None # Gender not available + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + """Convert clinical stage to binary: + 0 for T2 (less severe), 1 for T3/T4a (more severe)""" + if pd.isna(x) or not isinstance(x, str): + return None + value = x.split(': ')[-1].strip() + if 'T2' in value: + return 0 + elif 'T3' in value or 'T4a' in value: + return 1 + return None + +def convert_age(x): + return None # Not used since age data unavailable + +def convert_gender(x): + return None # Not used since gender data unavailable + +# 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: + selected_clinical = geo_select_clinical_features(clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait) + + print("Preview of processed clinical data:") + print(preview_df(selected_clinical)) + + # Save clinical data + selected_clinical.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# Extract mapping between gene IDs and gene symbols +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert from probes to genes +gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df) + +# Preview results +print("Shape of gene expression data after mapping:", gene_data.shape) +print("\nFirst few rows of mapped gene data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +# Remove "-mRNA" suffix from gene symbols before normalization +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data and trait +# First get selected clinical features using the extraction function from previous step +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 +) + +# Debug data structures before linking +print("\nPre-linking data shapes:") +print("Clinical data shape:", selected_clinical.shape) +print("Gene data shape:", gene_data.shape) +print("\nClinical data preview:") +print(selected_clinical.head()) + +# Transpose gene data to match clinical data orientation +gene_data_t = gene_data.T +linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate data quality 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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)." +) + +# 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/Bladder_Cancer/code/GSE245953.py b/p3/preprocess/Bladder_Cancer/code/GSE245953.py new file mode 100644 index 0000000000000000000000000000000000000000..01c77e8556fb87935255210a0c73026623534e8f --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE245953.py @@ -0,0 +1,190 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE245953" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE245953" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE245953.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE245953.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE245953.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Based on series summary mentioning "gene expression data", "microarray platform" and "full transcriptome analysis" +is_gene_available = True + +# 2.1 Data Availability +# Trait info available at row 0 - condition field +trait_row = 0 +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # Convert condition to binary: 1 for bladder cancer + if isinstance(x, str): + if 'bladder cancer' in x.lower(): + return 1 + return None + +def convert_age(x): + # Not needed since age data unavailable + return None + +def convert_gender(x): + # Not needed since gender data unavailable + return None + +# 3. Save initial filtering 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_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 clinical data +print("Clinical Data Preview:") +print(preview_df(clinical_df)) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# The gene expression data uses probe IDs from ID column in the gene expression data +# The gene symbols can be extracted from SPOT_ID.1 column in the gene annotation data + +# Get gene mapping dataframe +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1') + +# Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# Save gene expression data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# Print shape and preview +print("\nShape of mapped gene expression data:", gene_data.shape) +print("\nPreview of mapped gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols and save normalized gene data +# Remove "-mRNA" suffix from gene symbols before normalization +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data and trait +# First get selected clinical features using the extraction function from previous step +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 +) + +# Debug data structures before linking +print("\nPre-linking data shapes:") +print("Clinical data shape:", selected_clinical.shape) +print("Gene data shape:", gene_data.shape) +print("\nClinical data preview:") +print(selected_clinical.head()) + +# Transpose gene data to match clinical data orientation +gene_data_t = gene_data.T +linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate data quality 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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)." +) + +# 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/Bladder_Cancer/code/GSE253531.py b/p3/preprocess/Bladder_Cancer/code/GSE253531.py new file mode 100644 index 0000000000000000000000000000000000000000..0d22d10d4edbf2fc9b275bd0e11af52d1ffc1ff5 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/GSE253531.py @@ -0,0 +1,188 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" +cohort = "GSE253531" + +# Input paths +in_trait_dir = "../DATA/GEO/Bladder_Cancer" +in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE253531" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE253531.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE253531.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE253531.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# From background information, this dataset uses gene expression microarrays +is_gene_available = True + +# 2.1 Data Availability +# From Feature 1, we can infer tumor molecular subtypes which indicate bladder cancer status +trait_row = 1 +# 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 and strip whitespace + if ':' in str(x): + value = str(x).split(':')[1].strip().lower() + # Convert subtypes to binary - 1 for cancer samples (all samples here are cancer) + return 1 + return None + +def convert_age(x): + return None + +def convert_gender(x): + 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: + 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 data + print(preview_df(selected_clinical_df)) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Examining the row identifiers +# The identifiers seem to be numeric probe IDs (e.g., "2315554", "2315633") rather than human gene symbols +# These appear to be from a microarray platform and will need to be mapped to gene symbols +requires_gene_mapping = True +# Get file paths using library function +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview gene annotation data +print("Gene annotation columns and example values:") +print(preview_df(gene_annotation)) +# Looking at the gene annotation data, 'ID' matches the probe IDs in gene expression data +# The 'gene_assignment' column contains gene symbols embedded in longer descriptions + +# Get the gene mapping dataframe +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment') + +# Convert probe-level measurements to gene-level data using gene symbols extracted from descriptions +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# Print summary statistics before and after mapping to check the result +print("Number of unique gene symbols after mapping:", len(gene_data.index.unique())) +print("\nShape of mapped gene expression data:", gene_data.shape) +print("\nFirst few rows of mapped data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +# Remove "-mRNA" suffix from gene symbols before normalization +gene_data.index = gene_data.index.str.replace('-mRNA', '') +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data and trait +# First get selected clinical features using the extraction function from previous step +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 +) + +# Debug data structures before linking +print("\nPre-linking data shapes:") +print("Clinical data shape:", selected_clinical.shape) +print("Gene data shape:", gene_data.shape) +print("\nClinical data preview:") +print(selected_clinical.head()) + +# Transpose gene data to match clinical data orientation +gene_data_t = gene_data.T +linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1) + +# 3. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 4. Check for biased features and remove them if needed +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate data quality 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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)." +) + +# 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/Bladder_Cancer/code/TCGA.py b/p3/preprocess/Bladder_Cancer/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..36dc998b28c882511303c01362a817070277bed3 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/code/TCGA.py @@ -0,0 +1,93 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bladder_Cancer" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Bladder_Cancer/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json" + +# 1. Select the relevant subdirectory for bladder cancer +subdirectory = 'TCGA_Bladder_Cancer_(BLCA)' +cohort_dir = os.path.join(tcga_root_dir, subdirectory) + +# 2. Get the file paths +clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir) + +# 3. Load the data files +clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t') + +# 4. Print clinical data columns +print("Clinical data columns:") +print(clinical_df.columns.tolist()) +# Identify candidate columns +candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'age_began_smoking_in_years', 'days_to_birth'] +candidate_gender_cols = ['gender'] + +# Print directory contents to check structure +print("TCGA root directory contents:") +print(os.listdir(tcga_root_dir)) +# Dictionaries from previous steps showing age and gender column candidates +age_col_samples = { + 'age_at_initial_pathologic_diagnosis': [62, 68, 71, 69, 76], + 'age': [62, 68, 71, 69, 76] +} + +gender_col_samples = { + 'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE'] +} + +# Select appropriate columns based on data quality +age_col = 'age_at_initial_pathologic_diagnosis' # More specific clinical column name +gender_col = 'gender' # Only gender column available with valid values + +# Print chosen columns +print(f"Selected age column: {age_col}") +print(f"Selected gender column: {gender_col}") +# 1. Extract and standardize clinical features +# First create trait labels using sample IDs, then add demographics if available +clinical_features = tcga_select_clinical_features( + clinical_df, + trait=trait, + age_col='age_at_initial_pathologic_diagnosis', + gender_col='gender' +) + +# 2. Normalize gene symbols and save +normalized_gene_df = normalize_gene_symbols_in_index(genetic_df) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_gene_df.to_csv(out_gene_data_file) + +# 3. Link clinical and genetic data +linked_data = pd.concat([clinical_features, normalized_gene_df.T], axis=1) + +# 4. Handle missing values systematically +linked_data = handle_missing_values(linked_data, trait) + +# 5. Check for bias in trait and demographic features +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 6. Validate data quality and save cohort info +note = "Contains molecular data from tumor and normal samples with patient demographics." +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=trait_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) \ No newline at end of file diff --git a/p3/preprocess/Bladder_Cancer/cohort_info.json b/p3/preprocess/Bladder_Cancer/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..28a21a77e84bb7874be03555b0fa0326ae079da4 --- /dev/null +++ b/p3/preprocess/Bladder_Cancer/cohort_info.json @@ -0,0 +1 @@ +{"GSE253531": {"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": 54, "note": "Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."}, "GSE245953": {"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": 310, "note": "Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."}, "GSE244266": {"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": 189, "note": "Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."}, "GSE222073": {"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": 146, "note": "Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."}, "GSE203149": {"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": "All samples are cancer cases (no controls), making trait data unavailable for associative analysis."}, "GSE201395": {"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": "All samples are cancer cases (no controls), making trait data unavailable for associative analysis."}, "GSE185264": {"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": 48, "note": "NanoString nCounter RNA profiling data for bladder cancer recurrence study"}, "GSE162253": {"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": "NanoString nCounter RNA profiling data for bladder cancer recurrence study"}, "GSE145261": {"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": 22, "note": "NanoString nCounter RNA profiling data for bladder cancer recurrence study"}, "GSE138118": {"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": 55, "note": "NanoString nCounter RNA profiling data for bladder cancer recurrence study"}, "TCGA": {"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": 426, "note": "Contains molecular data from tumor and normal 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+BET1L,7.8575,7.9475,8.0425,8.035,8.215,8.325,7.815,8.0875,7.890000000000001,8.255,8.504999999999999,7.800000000000001,7.6825,7.5249999999999995,7.632499999999999,7.950000000000001,8.06,7.925,7.6825,7.79,7.834999999999999,7.915000000000001,7.6525,7.8375,7.14,7.0775,7.1175,7.047499999999999,6.802499999999999,7.09,7.0325,7.012499999999999,7.1125 +BEX1,1.3875,1.3875,1.355,1.4325,1.42,1.385,1.4025,1.385,1.3975,1.3525,1.4325,1.3325,1.38,1.3475,1.34,1.405,1.36,1.405,1.4075,1.445,1.4,1.4175,1.3925,1.4,1.4825,1.4525,1.43,1.415,1.4175,1.445,1.5025,1.4375,1.4125 +BEX2,1.3,1.2825,1.275,1.345,1.355,1.3525,1.36,1.2775,1.285,1.2825,1.365,1.2775,1.3025,1.36,1.285,1.295,1.315,1.3825,1.3275,1.365,1.315,1.2925,1.3225,1.32,1.585,1.3775,1.355,1.355,1.35,1.3525,1.39,1.325,1.3925 +BEX3,1.905,1.86,1.88,2.045,2.0475,2.075,1.92,1.8725,1.825,1.9,1.805,1.8275,1.9325,1.9875,1.9675,2.0125,1.945,1.965,2.055,2.0225,2.07,2.03,2.0775,2.03,2.06,2.27,2.27,2.15,2.13,2.06,2.1675,2.2125,2.1375 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+CAPN12,4.75,4.75,4.72,4.67,4.69,4.7,4.69,4.6,4.8,4.7,4.67,4.59,4.59,4.6,4.64,4.59,4.66,4.75,4.71,4.66,4.67,4.57,4.67,4.67,4.73,4.5,4.6,4.67,4.76,4.67,4.75,4.68,4.78 +CAPN13,1.8166666666666667,1.9100000000000001,1.9233333333333331,1.86,1.87,1.9733333333333334,1.8566666666666667,1.87,1.8366666666666667,1.9233333333333331,1.9533333333333334,2.1766666666666667,2.0366666666666666,1.9233333333333331,1.9566666666666668,2.08,2.143333333333333,1.9566666666666668,1.75,1.8466666666666667,1.7066666666666668,1.68,1.7233333333333334,1.7733333333333334,1.8633333333333333,2.103333333333333,2.3133333333333335,1.86,1.87,1.9233333333333331,2.1233333333333335,2.13,2.0100000000000002 +CAPN14,2.245,2.225,2.15,2.26,2.24,2.285,2.35,2.16,2.29,2.25,2.24,2.17,2.19,2.22,2.185,2.11,2.19,2.21,2.25,2.26,2.215,2.155,2.305,2.245,2.335,2.355,2.415,2.65,2.515,2.44,2.805,2.925,2.745 +CAPN15,3.245,3.14,3.15,3.0,3.065,3.005,3.075,3.15,3.165,3.0,3.05,3.025,3.29,3.095,3.19,3.075,3.065,3.08,3.225,3.155,3.085,3.19,3.16,3.11,2.91,2.92,2.955,3.015,3.045,3.015,2.975,2.96,3.015 +CAPN2,2.8925,2.885,2.89,2.7925,2.87,2.8,2.89,2.8925,2.83,3.0975,3.03,3.075,2.9825,2.7325,2.7975,2.955,2.91,2.86,2.78,2.66,2.7675,2.9525,2.8275,2.7475,2.2625,2.07,2.035,2.2975,2.1775,2.1475,2.3225,2.2875,2.2925 +CAPN3,1.5966666666666667,1.5933333333333335,1.5733333333333333,1.6433333333333333,1.6533333333333333,1.6733333333333331,1.62,1.5999999999999999,1.6433333333333333,1.7300000000000002,1.8133333333333335,1.6433333333333333,1.58,1.5566666666666666,1.61,1.6933333333333334,1.7633333333333334,1.6433333333333333,1.5899999999999999,1.6066666666666667,1.5566666666666666,1.5533333333333335,1.5999999999999999,1.57,1.7433333333333334,1.6466666666666667,1.75,1.6566666666666665,1.6566666666666665,1.68,1.7333333333333334,1.7233333333333334,1.78 +CAPN5,3.305,3.475,3.575,3.83,3.86,3.995,2.98,3.105,3.145,3.465,3.53,3.31,2.985,3.035,3.005,3.35,3.295,3.39,3.19,3.275,3.085,3.065,3.16,3.36,3.745,3.585,3.66,3.295,3.355,3.38,3.48,3.485,3.405 +CAPN6,1.46,1.4833333333333334,1.4400000000000002,1.53,1.4666666666666668,1.5033333333333332,1.5333333333333332,1.5,1.5,2.4933333333333336,2.1,2.2966666666666664,1.4800000000000002,1.4033333333333333,1.4333333333333333,3.013333333333333,2.6166666666666667,1.75,1.5133333333333334,1.4733333333333334,1.4400000000000002,1.4866666666666666,1.4866666666666666,1.4733333333333334,1.5033333333333332,1.4366666666666665,1.45,1.4466666666666665,1.5066666666666666,1.51,1.5066666666666666,1.4966666666666668,1.4800000000000002 +CAPN7,3.3233333333333337,3.3166666666666664,3.3066666666666666,3.3366666666666664,3.34,3.3333333333333335,3.34,3.3200000000000003,3.3166666666666664,3.356666666666667,3.3433333333333333,3.3266666666666667,3.3866666666666667,3.2866666666666666,3.3200000000000003,3.34,3.3366666666666664,3.376666666666667,3.276666666666667,3.2433333333333336,3.33,3.3800000000000003,3.34,3.2933333333333334,3.5733333333333337,3.4766666666666666,3.41,3.3200000000000003,3.3466666666666662,3.3366666666666664,3.35,3.3699999999999997,3.3466666666666662 +CAPN8,2.77,2.8833333333333333,2.9299999999999997,2.54,2.6566666666666667,2.65,2.09,2.23,2.1966666666666668,1.87,1.9933333333333334,1.8433333333333335,2.2733333333333334,2.2433333333333336,2.11,1.8399999999999999,1.9233333333333331,1.8533333333333333,2.36,2.4966666666666666,2.5166666666666666,2.263333333333333,2.0533333333333332,2.1266666666666665,2.2733333333333334,2.5066666666666664,2.63,2.14,2.1133333333333333,2.17,1.9800000000000002,2.0366666666666666,1.9733333333333334 +CAPN9,1.46,1.4666666666666668,1.43,1.5433333333333332,1.5933333333333335,1.61,1.4866666666666666,1.4033333333333333,1.4533333333333334,1.5533333333333335,1.49,1.4433333333333334,1.4100000000000001,1.4433333333333334,1.4233333333333331,1.3733333333333333,1.4266666666666667,1.4533333333333334,1.4533333333333334,1.4100000000000001,1.4066666666666665,1.3733333333333333,1.45,1.4466666666666665,1.5199999999999998,1.4133333333333333,1.4266666666666667,1.39,1.4833333333333334,1.4833333333333334,1.4633333333333332,1.4533333333333334,1.4533333333333334 +CAPNS1,1.6644444444444444,1.6966666666666665,1.7033333333333334,1.6722222222222223,1.7011111111111112,1.6866666666666665,1.6055555555555554,1.67,1.6233333333333333,1.6266666666666667,1.6255555555555556,1.6255555555555556,1.6366666666666667,1.6222222222222222,1.6344444444444446,1.6633333333333333,1.61,1.6177777777777778,1.6177777777777778,1.6344444444444446,1.6433333333333333,1.6144444444444443,1.6066666666666667,1.6222222222222222,1.54,1.61,1.57,1.5344444444444445,1.58,1.5744444444444445,1.588888888888889,1.5955555555555554,1.5644444444444445 +CAPNS2,3.6733333333333333,3.89,3.8633333333333333,4.3500000000000005,4.07,4.3933333333333335,3.5533333333333332,3.4566666666666666,3.4133333333333336,2.686666666666667,2.4533333333333336,2.4033333333333333,3.1466666666666665,3.66,3.5,2.8699999999999997,2.9633333333333334,2.9033333333333338,3.6799999999999997,4.19,3.78,3.4499999999999997,3.4333333333333336,3.98,2.066666666666667,2.9433333333333334,3.1166666666666667,3.0733333333333337,2.5533333333333332,2.65,3.1466666666666665,3.0833333333333335,2.9499999999999997 +CAPRIN1,7.5,7.41,7.495,7.145,7.205,7.17,7.35,7.51,7.345,7.3,7.24,7.285,7.435,7.41,7.455,7.475,7.35,7.37,7.36,7.295,7.405,7.4,7.345,7.26,7.5,7.635,7.535,7.48,7.615,7.615,7.635,7.7,7.655 +CAPRIN2,8.35,8.756666666666666,8.626666666666667,8.853333333333333,8.836666666666666,9.183333333333334,9.33,8.61,9.246666666666666,8.816666666666666,8.903333333333334,8.799999999999999,8.87,8.81,8.76,8.47,9.05,8.856666666666667,8.573333333333334,8.493333333333334,8.423333333333334,8.993333333333334,8.516666666666666,8.696666666666665,9.406666666666666,8.636666666666667,9.203333333333333,8.886666666666667,9.11,8.976666666666667,10.816666666666666,10.563333333333333,10.45 +CAPS,2.8033333333333332,2.9133333333333336,2.9966666666666666,3.0233333333333334,3.08,3.0766666666666667,2.796666666666667,3.0433333333333334,2.8266666666666667,3.3000000000000003,3.186666666666667,3.0966666666666662,2.5733333333333333,2.9566666666666666,2.9266666666666663,3.1,2.9333333333333336,3.0833333333333335,2.76,3.0033333333333334,2.893333333333333,2.486666666666667,2.5633333333333335,2.7266666666666666,2.4633333333333334,2.393333333333333,2.5366666666666666,2.3233333333333333,2.2533333333333334,2.393333333333333,2.3266666666666667,2.3133333333333335,2.4033333333333333 +CAPS2,1.72,1.6866666666666665,1.6933333333333334,1.87,1.9400000000000002,1.7766666666666666,1.75,1.7233333333333334,1.6833333333333333,1.7833333333333332,1.7866666666666668,1.7533333333333332,1.6500000000000001,1.6500000000000001,1.6933333333333334,1.8133333333333335,1.7866666666666668,1.7966666666666666,1.9233333333333331,1.83,1.9433333333333334,1.8766666666666667,1.9666666666666668,1.9233333333333331,1.6066666666666667,1.55,1.5599999999999998,1.67,1.5833333333333333,1.6733333333333331,1.7966666666666666,1.7699999999999998,1.71 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+CAPZA3,1.3166666666666667,1.27,1.2833333333333334,1.2466666666666668,1.4733333333333334,1.2533333333333332,1.2833333333333334,1.3133333333333332,1.4866666666666666,1.2333333333333334,1.3466666666666667,1.2833333333333334,1.3433333333333335,1.2666666666666666,1.4266666666666667,1.28,1.3066666666666666,1.33,1.3466666666666667,1.38,1.3533333333333333,1.33,1.4133333333333333,1.4166666666666667,1.3966666666666667,1.27,1.3366666666666667,1.33,1.32,1.26,1.4000000000000001,1.2433333333333334,1.4333333333333333 +CAPZB,1.886,1.8980000000000001,1.9140000000000001,1.826,1.85,1.834,1.89,1.906,1.8920000000000001,1.9100000000000001,1.866,1.9760000000000002,1.8699999999999999,1.8539999999999999,1.86,1.938,1.8960000000000001,1.9300000000000002,1.8379999999999999,1.8379999999999999,1.8359999999999999,1.86,1.8760000000000001,1.8519999999999999,1.8379999999999999,1.866,1.85,1.8399999999999999,1.85,1.8239999999999998,1.8579999999999999,1.886,1.8399999999999999 +CARD10,1.665,1.595,1.6275,1.44,1.4725,1.4475,1.685,1.59,1.6075,1.4525,1.495,1.5075,1.7425,1.58,1.6025,1.455,1.4675,1.5125,1.6225,1.565,1.6175,1.645,1.595,1.5,1.3925,1.3825,1.3625,1.41,1.405,1.4025,1.3975,1.385,1.4125 +CARD11,2.85,2.9466666666666668,2.983333333333333,2.736666666666667,2.7066666666666666,2.84,2.5633333333333335,2.62,2.533333333333333,2.3666666666666667,2.4433333333333334,2.3266666666666667,2.736666666666667,2.73,2.74,2.65,2.5433333333333334,2.54,2.796666666666667,2.8533333333333335,2.75,2.716666666666667,2.6799999999999997,2.6766666666666663,2.59,2.78,2.723333333333333,2.4433333333333334,2.5566666666666666,2.52,2.3633333333333333,2.3866666666666667,2.32 +CARD14,2.57,2.565,2.585,2.595,2.57,2.61,2.7,2.58,2.59,2.555,2.59,2.615,2.595,2.595,2.59,2.525,2.655,2.595,2.595,2.6,2.585,2.595,2.595,2.65,2.595,2.48,2.56,2.59,2.595,2.6,2.685,2.615,2.66 +CARD16,5.09,5.25,4.86,5.91,5.61,5.95,5.54,5.39,5.69,5.76,6.4,5.63,5.43,5.9,5.63,7.29,6.79,7.15,5.56,6.08,5.84,5.52,6.19,6.46,5.45,6.21,6.3,7.23,7.02,7.37,7.85,7.67,7.63 +CARD17P,4.69,4.76,4.92,5.09,4.44,4.94,4.77,4.85,4.92,4.55,4.73,4.54,4.56,4.93,4.46,6.06,5.83,5.37,4.71,4.6,5.11,4.82,5.04,4.79,5.07,4.35,4.69,6.91,5.96,6.32,7.22,6.45,6.36 +CARD18,2.095,1.985,1.89,2.07,1.845,2.035,2.135,1.99,1.94,2.035,1.92,2.005,2.005,1.93,2.025,1.95,1.91,2.14,2.02,2.16,1.945,1.92,2.02,2.13,2.04,2.035,1.905,1.89,2.01,2.03,1.965,1.98,2.075 +CARD19,1.6875,1.685,1.71,1.715,1.7375,1.74,1.63,1.71,1.6475,1.6825,1.6475,1.725,1.7675,1.7,1.705,1.7575,1.6875,1.68,1.7725,1.745,1.7725,1.8075,1.775,1.71,1.665,1.605,1.5525,1.645,1.615,1.5175,1.575,1.56,1.575 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+CARINH,1.7266666666666666,1.7300000000000002,1.6466666666666667,1.8366666666666667,1.83,1.9633333333333332,1.7466666666666668,1.7366666666666666,1.71,1.8499999999999999,1.8333333333333333,1.7766666666666666,1.71,1.7233333333333334,1.7266666666666666,1.8633333333333333,1.9066666666666665,1.8466666666666667,1.7466666666666668,1.7466666666666668,1.71,1.7266666666666666,1.7333333333333334,1.8333333333333333,1.7233333333333334,1.6666666666666667,1.6500000000000001,1.7066666666666668,1.6900000000000002,1.6966666666666665,1.7666666666666666,1.72,1.7566666666666666 +CARM1,9.81,9.59,9.8,8.91,9.11,9.07,10.79,11.15,10.91,10.52,10.42,10.7,11.07,10.64,10.93,10.02,9.65,9.83,9.75,9.56,9.89,9.61,9.39,8.86,9.34,9.89,9.44,9.34,9.34,9.21,9.1,9.18,9.01 +CARM1P1,4.45,4.17,4.58,4.28,4.46,4.04,4.24,4.18,4.3,4.33,4.16,4.09,4.24,3.96,4.26,3.99,4.31,4.34,4.24,4.12,4.25,4.44,4.01,4.5,4.62,4.19,3.82,4.16,4.27,4.46,4.51,4.11,4.32 +CARMIL1,2.9433333333333334,2.9499999999999997,2.9766666666666666,3.02,3.013333333333333,3.08,2.97,3.1966666666666668,3.0366666666666666,3.013333333333333,3.0433333333333334,3.1133333333333333,3.0066666666666664,3.0933333333333333,3.0966666666666662,3.046666666666667,2.99,3.013333333333333,3.01,3.06,3.013333333333333,2.9466666666666668,2.986666666666667,3.0733333333333337,2.5366666666666666,2.9766666666666666,2.9566666666666666,2.8066666666666666,2.8200000000000003,2.7866666666666666,3.2933333333333334,3.27,3.17 +CARMIL2,2.54,2.475,2.475,2.44,2.425,2.49,2.46,2.46,2.49,2.485,2.46,2.49,2.47,2.435,2.465,2.41,2.435,2.43,2.47,2.435,2.455,2.47,2.46,2.45,2.49,2.42,2.42,2.46,2.475,2.47,2.46,2.46,2.47 +CARMIL3,4.42,4.33,4.35,4.31,4.24,4.33,4.45,4.32,4.41,4.26,4.32,4.4,4.29,4.21,4.33,4.24,4.25,4.33,4.35,4.3,4.23,4.29,4.33,4.36,4.43,4.22,4.28,4.35,4.47,4.46,4.46,4.38,4.43 +CARMN,0.95,0.9400000000000001,0.95,0.95,0.95,0.9400000000000001,0.9720000000000001,0.932,0.9720000000000001,0.9380000000000001,0.95,0.9380000000000001,0.9339999999999999,0.9460000000000001,0.914,0.9380000000000001,0.974,0.9560000000000001,0.9560000000000001,0.954,0.9380000000000001,0.9400000000000001,0.97,0.95,0.968,0.924,0.9279999999999999,0.974,0.962,0.96,0.97,0.976,0.95 +CARNMT1,3.5766666666666667,3.4633333333333334,3.5533333333333332,3.1533333333333338,3.1799999999999997,3.08,3.69,3.5666666666666664,3.5366666666666666,3.09,3.0033333333333334,3.08,3.6,3.65,3.7100000000000004,3.22,3.1733333333333333,3.3200000000000003,3.5966666666666662,3.526666666666667,3.7100000000000004,3.6199999999999997,3.52,3.3200000000000003,3.2899999999999996,3.3666666666666667,3.356666666666667,3.3866666666666667,3.4633333333333334,3.4766666666666666,3.25,3.28,3.2433333333333336 +CARNS1,5.61,5.61,5.47,5.47,5.47,5.47,5.44,5.44,5.53,5.53,5.59,5.5,5.45,5.42,5.5,5.49,5.39,5.47,5.47,5.38,5.37,5.43,5.5,5.37,5.53,5.39,5.45,5.43,5.57,5.47,5.47,5.47,5.58 +CARS1,5.096666666666668,5.071666666666667,4.971666666666667,5.071666666666667,5.098333333333334,5.086666666666667,5.1,5.21,5.121666666666666,5.033333333333333,5.1899999999999995,4.911666666666667,5.118333333333334,5.215,5.12,5.171666666666667,5.0,5.128333333333333,4.983333333333333,5.43,5.238333333333333,5.118333333333334,5.04,5.046666666666667,5.436666666666667,5.453333333333333,5.404999999999999,5.4383333333333335,5.665,5.628333333333333,5.373333333333333,5.4,5.571666666666667 +CARS1-AS1,2.7,2.685,2.555,2.675,2.685,2.66,2.68,2.78,2.795,2.65,2.61,2.475,2.615,2.705,2.66,2.605,2.57,2.655,2.56,2.78,2.765,2.615,2.62,2.66,2.64,2.65,2.655,2.575,2.805,2.755,2.67,2.57,2.785 +CARS2,2.4833333333333334,2.52,2.4966666666666666,2.4166666666666665,2.4566666666666666,2.3733333333333335,2.5633333333333335,2.5233333333333334,2.5533333333333332,2.59,2.4933333333333336,2.5966666666666667,2.7466666666666666,2.63,2.606666666666667,2.64,2.5766666666666667,2.6566666666666667,2.56,2.53,2.643333333333333,2.6733333333333333,2.6233333333333335,2.5233333333333334,2.42,2.5233333333333334,2.4833333333333334,2.353333333333333,2.4466666666666668,2.356666666666667,2.3733333333333335,2.3633333333333333,2.35 +CARTPT,3.3150000000000004,3.1899999999999995,3.1775,3.2475,3.2900000000000005,3.25,3.5300000000000002,3.1225,3.38,2.9175,3.245,3.2,3.115,3.2075,3.125,3.0925000000000002,3.1700000000000004,3.1374999999999997,3.3,3.3125,3.1625,3.1675000000000004,3.3025,3.26,3.4025,3.1325,3.1175,3.1525000000000003,3.2974999999999994,3.2925,3.3599999999999994,3.2150000000000003,3.3825 +CASC2,2.68,2.8,2.7,2.775,2.83,2.825,2.575,2.66,2.82,2.525,2.61,2.51,2.64,2.575,2.62,2.65,2.525,2.575,2.755,2.64,2.65,2.78,2.725,2.685,2.655,2.46,2.55,2.65,2.655,2.65,2.65,2.62,2.555 +CASC3,3.0025,2.965,3.0125,2.84,2.9025,2.8525,3.0575,3.0825,3.01,3.0075,3.02,3.0425,3.045,2.98,3.0225,2.9575,2.9275,3.0025,2.915,2.87,2.9175,2.945,2.8975,2.855,2.815,2.9225,2.84,2.745,2.8275,2.7975,2.8475,2.88,2.835 +CASD1,2.18,2.29,2.2525,2.195,2.2375,2.1825,1.9125,1.975,1.8375,2.1625,2.195,2.045,1.9325,1.9025,1.92,2.1975,2.18,2.1525,2.025,2.075,2.08,1.97,2.0225,1.995,2.545,2.6075,2.6625,2.4425,2.47,2.555,2.355,2.33,2.3025 +CASK,12.473333333333333,12.586666666666666,12.686666666666667,12.846666666666668,12.75,12.908333333333333,12.670000000000002,12.598333333333333,12.476666666666667,12.443333333333333,12.658333333333333,13.055,12.506666666666668,12.748333333333333,13.043333333333333,12.701666666666666,12.511666666666667,12.948333333333334,12.753333333333334,13.116666666666667,12.731666666666666,12.886666666666667,12.993333333333334,13.141666666666666,12.99,13.106666666666666,12.616666666666667,12.938333333333333,12.856666666666666,12.878333333333334,12.798333333333332,12.623333333333333,13.111666666666666 +CASK-AS1,1.6833333333333333,1.7766666666666666,1.6433333333333333,1.8233333333333333,1.7033333333333334,1.7833333333333332,1.5633333333333335,1.6233333333333333,1.6566666666666665,1.4366666666666665,1.57,1.5266666666666666,1.8233333333333333,2.0166666666666666,1.7933333333333332,1.6900000000000002,1.6500000000000001,1.82,1.75,1.8499999999999999,1.8866666666666667,1.82,1.8333333333333333,1.8066666666666666,1.6133333333333333,1.7566666666666666,1.6266666666666667,1.7766666666666666,1.8433333333333335,1.7633333333333334,1.8333333333333333,1.7366666666666666,1.7566666666666666 +CASKIN1,2.74,2.67,2.74,2.685,2.725,2.78,2.7,2.655,2.65,2.67,2.705,2.65,2.61,2.665,2.65,2.675,2.685,2.67,2.68,2.695,2.67,2.685,2.675,2.735,2.67,2.6,2.565,2.67,2.675,2.655,2.675,2.67,2.67 +CASKIN2,2.1666666666666665,2.1766666666666667,2.22,2.2066666666666666,2.3133333333333335,2.2666666666666666,2.0733333333333333,2.2033333333333336,2.0966666666666667,2.1999999999999997,2.34,2.29,2.14,2.16,2.1266666666666665,2.2733333333333334,2.183333333333333,2.1966666666666668,2.1533333333333333,2.066666666666667,2.13,2.1666666666666665,2.18,2.15,2.2733333333333334,2.276666666666667,2.1999999999999997,2.1233333333333335,2.183333333333333,2.183333333333333,2.143333333333333,2.1966666666666668,2.1766666666666667 +CASP1,1.8533333333333333,1.9233333333333331,1.8366666666666667,2.15,2.1866666666666665,2.31,2.0766666666666667,2.0833333333333335,2.0566666666666666,2.183333333333333,2.276666666666667,2.026666666666667,2.11,2.3833333333333333,2.3333333333333335,2.6766666666666663,2.646666666666667,2.5766666666666667,2.33,2.5366666666666666,2.3733333333333335,2.2566666666666664,2.473333333333333,2.67,2.0500000000000003,2.58,2.5833333333333335,3.0866666666666664,3.0700000000000003,2.9966666666666666,3.1733333333333333,3.126666666666667,3.126666666666667 +CASP10,1.9733333333333334,2.013333333333333,2.0466666666666664,2.22,2.27,2.3033333333333332,2.11,2.1999999999999997,2.0733333333333333,2.3133333333333335,2.2866666666666666,2.14,2.27,2.3133333333333335,2.2533333333333334,2.9499999999999997,2.8000000000000003,2.6933333333333334,2.066666666666667,2.2266666666666666,2.103333333333333,2.103333333333333,2.1866666666666665,2.4033333333333333,2.22,2.28,2.3866666666666667,2.29,2.27,2.25,2.6533333333333333,2.6566666666666667,2.64 +CASP12,4.609999999999999,4.545,4.545,4.470000000000001,4.445,4.47,4.465,4.41,4.58,4.62,4.615,4.42,4.375,4.55,4.470000000000001,4.525,4.515,4.625,4.470000000000001,4.630000000000001,4.365,4.46,4.57,4.734999999999999,4.58,4.449999999999999,4.455,4.395,4.535,4.65,4.619999999999999,4.57,4.575 +CASP14,1.4733333333333334,1.4633333333333332,1.6066666666666667,1.5233333333333334,1.49,1.5633333333333335,1.5133333333333334,1.5199999999999998,1.5133333333333334,1.5266666666666666,1.5133333333333334,1.5566666666666666,1.4866666666666666,1.7766666666666666,1.5599999999999998,1.4266666666666667,1.47,1.5133333333333334,1.5533333333333335,1.87,1.55,1.5033333333333332,1.5566666666666666,1.5533333333333335,1.5533333333333335,1.57,1.7766666666666666,1.7333333333333334,1.5966666666666667,1.8166666666666667,1.64,1.5933333333333335,1.6233333333333333 +CASP16P,4.13,4.36,4.18,4.29,4.3,4.43,4.31,4.22,4.35,4.43,4.37,4.24,4.29,4.29,4.18,4.18,4.29,4.28,4.36,4.27,4.27,4.27,4.19,4.41,4.33,4.22,4.2,4.29,4.3,4.38,4.56,4.35,4.29 +CASP2,4.660666666666667,4.7059999999999995,4.788,4.354666666666667,4.4526666666666666,4.403333333333333,4.6546666666666665,4.770666666666666,4.758,5.073333333333333,4.9286666666666665,5.24,4.514666666666667,4.422666666666666,4.534666666666666,4.718666666666667,4.786,4.744666666666666,4.426666666666667,4.483333333333333,4.499333333333333,4.523333333333333,4.471333333333333,4.466,4.3533333333333335,4.41,4.3020000000000005,4.328,4.422,4.455333333333333,4.374666666666666,4.328,4.366666666666666 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+CASP9,1.825,1.86,1.8525,1.95,1.94,1.965,1.8375,1.8425,1.8225,1.8475,1.855,1.8,1.78,1.84,1.83,1.82,1.845,1.8575,1.85,1.8725,1.835,1.8125,1.8325,1.865,1.9175,1.88,1.8825,1.9025,1.865,1.89,1.855,1.8675,1.91 +CASQ1,2.1366666666666667,2.0966666666666667,2.0566666666666666,2.58,2.646666666666667,2.7433333333333336,2.2133333333333334,2.0966666666666667,2.183333333333333,2.36,2.876666666666667,2.1166666666666667,2.0733333333333333,2.183333333333333,2.2733333333333334,2.3633333333333333,2.486666666666667,2.34,2.2066666666666666,2.3233333333333333,2.2066666666666666,2.1566666666666667,2.21,2.5100000000000002,2.513333333333333,2.736666666666667,2.9933333333333336,2.763333333333333,2.5433333333333334,2.5866666666666664,3.2100000000000004,3.0566666666666666,3.1333333333333333 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+CHMP4B,4.5200000000000005,4.613333333333333,4.63,4.713333333333334,4.7299999999999995,4.806666666666667,4.306666666666667,4.503333333333333,4.196666666666666,4.46,4.516666666666667,4.343333333333333,4.483333333333333,4.566666666666666,4.5200000000000005,4.673333333333333,4.513333333333333,4.54,4.526666666666666,4.593333333333333,4.6033333333333335,4.486666666666667,4.47,4.576666666666667,4.3533333333333335,4.483333333333333,4.39,4.45,4.4799999999999995,4.47,4.406666666666667,4.49,4.416666666666667 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+CHRM3,8.716666666666665,8.46,8.553333333333335,9.346666666666668,9.22,8.896666666666667,9.93,8.57,8.983333333333334,8.176666666666666,8.76,8.91,8.276666666666667,9.036666666666667,8.706666666666667,8.726666666666667,8.853333333333333,8.923333333333334,9.273333333333333,9.35,8.823333333333334,8.636666666666667,9.14,9.540000000000001,9.67,8.803333333333335,8.623333333333333,9.26,9.02,8.953333333333333,9.616666666666667,9.196666666666665,9.423333333333334 +CHRM3-AS1,1.6866666666666665,1.57,1.5666666666666667,1.5266666666666666,1.7233333333333334,1.55,1.7866666666666668,1.51,1.5033333333333332,1.5233333333333334,1.63,1.6733333333333331,1.4566666666666668,1.5999999999999999,1.45,1.5999999999999999,1.46,1.5233333333333334,1.5999999999999999,1.5666666666666667,1.62,1.4833333333333334,1.5433333333333332,1.6266666666666667,1.6433333333333333,1.5633333333333335,1.4766666666666666,1.6033333333333333,1.57,1.4666666666666668,1.58,1.6466666666666667,1.5633333333333335 +CHRM3-AS2,1.4766666666666666,1.4733333333333334,1.3733333333333333,1.4466666666666665,1.46,1.4333333333333333,1.5566666666666666,1.4433333333333334,1.47,1.4333333333333333,1.3133333333333332,1.4233333333333331,1.4033333333333333,1.4466666666666665,1.4166666666666667,1.4033333333333333,1.3733333333333333,1.4166666666666667,1.4766666666666666,1.4733333333333334,1.4533333333333334,1.4566666666666668,1.3966666666666667,1.4266666666666667,1.4433333333333334,1.42,1.4266666666666667,1.45,1.43,1.4233333333333331,1.3966666666666667,1.43,1.4333333333333333 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+CHRNA10,1.6166666666666665,1.6266666666666667,1.5466666666666666,1.55,1.4866666666666666,1.55,1.4733333333333334,1.5733333333333333,1.63,1.5966666666666667,1.5,1.58,1.5266666666666666,1.5266666666666666,1.5466666666666666,1.4800000000000002,1.51,1.5199999999999998,1.5433333333333332,1.5466666666666666,1.5433333333333332,1.5599999999999998,1.5466666666666666,1.53,1.5866666666666667,1.5166666666666666,1.5566666666666666,1.5466666666666666,1.5566666666666666,1.5366666666666668,1.55,1.5333333333333332,1.5899999999999999 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+CHRNA4,3.956666666666667,4.003333333333333,3.953333333333333,4.15,4.118333333333333,4.131666666666667,4.301666666666667,3.845,4.156666666666666,4.016666666666667,4.078333333333333,3.94,3.98,4.12,4.003333333333333,3.9433333333333334,4.098333333333334,4.158333333333333,4.171666666666667,4.0183333333333335,4.031666666666666,4.046666666666667,4.3133333333333335,4.213333333333333,4.241666666666667,4.046666666666667,3.9883333333333333,4.073333333333333,4.123333333333333,4.295,4.433333333333334,4.183333333333334,4.303333333333333 +CHRNA5,3.3333333333333335,3.313333333333333,3.263333333333333,2.47,2.67,2.53,3.3266666666666667,3.3233333333333337,3.2533333333333334,2.4266666666666667,2.2933333333333334,3.1333333333333333,3.3833333333333333,3.3966666666666665,3.47,2.8833333333333333,2.7933333333333334,3.1,3.36,3.373333333333333,3.453333333333333,3.4033333333333338,3.3266666666666667,3.11,3.0533333333333332,3.5966666666666662,3.4266666666666663,3.3800000000000003,3.513333333333333,3.516666666666667,2.9266666666666663,3.1,2.983333333333333 +CHRNA6,2.085,2.08,2.065,2.12,2.12,2.115,2.095,2.225,2.265,2.14,2.09,2.005,2.12,2.13,2.185,2.045,2.08,2.185,2.28,2.12,2.01,2.085,2.12,2.31,2.135,2.03,2.12,2.08,2.13,2.115,2.21,2.075,2.12 +CHRNA7,5.836666666666667,5.7299999999999995,5.733333333333334,5.71,5.6866666666666665,5.736666666666667,5.615,5.6866666666666665,5.903333333333334,5.661666666666667,5.6033333333333335,5.705,5.586666666666667,5.541666666666667,5.623333333333333,5.671666666666667,5.678333333333334,5.611666666666666,5.726666666666667,5.665000000000001,5.535,5.636666666666667,5.888333333333334,5.666666666666667,6.8,6.796666666666666,6.5,6.275,6.673333333333333,6.65,6.74,6.995000000000001,6.84 +CHRNA9,1.3366666666666667,1.3433333333333335,1.31,1.33,1.3433333333333335,1.33,1.4366666666666665,1.2866666666666666,1.37,1.3566666666666667,1.33,1.3166666666666667,1.36,1.33,1.33,1.3566666666666667,1.3533333333333333,1.2866666666666666,1.3566666666666667,1.4066666666666665,1.3566666666666667,1.3333333333333333,1.33,1.2833333333333334,1.33,1.24,1.2533333333333332,1.31,1.33,1.3399999999999999,1.32,1.3133333333333332,1.33 +CHRNB1,1.3,1.28,1.2875,1.3925,1.3875,1.35,1.3325,1.32,1.305,1.3675,1.36,1.34,1.33,1.285,1.3225,1.35,1.345,1.3675,1.305,1.3625,1.325,1.33,1.38,1.37,1.345,1.265,1.3175,1.3075,1.29,1.3075,1.3275,1.345,1.3775 +CHRNB2,1.7166666666666668,1.68,1.6466666666666667,1.5866666666666667,1.5966666666666667,1.6066666666666667,1.6666666666666667,1.6466666666666667,1.8099999999999998,1.6933333333333334,1.6966666666666665,1.64,1.6066666666666667,1.6266666666666667,1.6600000000000001,1.64,1.5999999999999999,1.6366666666666667,1.7066666666666668,1.5866666666666667,1.6466666666666667,1.6166666666666665,1.6466666666666667,1.62,1.7033333333333334,1.6333333333333335,1.68,1.62,1.7033333333333334,1.6833333333333333,1.5899999999999999,1.6500000000000001,1.6766666666666667 +CHRNB3,2.065,2.105,2.09,2.115,2.105,2.11,2.21,2.05,2.205,2.19,2.16,2.125,2.105,2.03,2.075,2.08,2.105,2.045,2.165,2.09,2.06,2.08,2.225,2.105,2.105,1.965,2.09,2.095,2.13,2.15,2.195,2.155,2.105 +CHRNB4,7.026666666666666,7.726666666666667,7.143333333333333,7.946666666666666,7.386666666666667,7.523333333333333,7.699999999999999,7.376666666666666,7.443333333333333,7.34,7.323333333333333,7.046666666666666,7.116666666666667,7.426666666666667,7.243333333333333,7.050000000000001,7.093333333333334,7.336666666666667,7.676666666666667,7.286666666666666,7.296666666666666,7.306666666666667,7.256666666666666,7.386666666666667,7.676666666666667,6.86,6.796666666666666,6.976666666666667,7.496666666666666,7.63,7.36,7.086666666666666,7.346666666666667 +CHRND,6.335,6.289999999999999,6.39,6.172499999999999,6.2225,6.175,6.4375,6.09,6.12,5.9975000000000005,5.8774999999999995,6.205,6.092499999999999,6.297499999999999,6.055000000000001,6.025,6.2275,6.265,6.12,6.09,6.205,5.9475,6.109999999999999,6.305,6.484999999999999,5.9925,6.2725,6.095000000000001,6.4725,6.68,6.27,6.4125000000000005,6.33 +CHRNE,2.46,2.535,2.425,2.42,2.425,2.43,2.455,2.45,2.46,2.445,2.5,2.42,2.385,2.42,2.4,2.42,2.42,2.345,2.47,2.39,2.37,2.365,2.42,2.445,2.42,2.355,2.35,2.42,2.415,2.385,2.475,2.4,2.445 +CHRNG,1.7533333333333332,1.7466666666666668,1.7366666666666666,1.6066666666666667,1.6866666666666665,1.6266666666666667,1.7066666666666668,1.7033333333333334,1.7366666666666666,1.6766666666666667,1.6900000000000002,1.6766666666666667,1.6500000000000001,1.6766666666666667,1.6666666666666667,1.5999999999999999,1.6566666666666665,1.6600000000000001,1.7233333333333334,1.6133333333333333,1.6733333333333331,1.6566666666666665,1.6766666666666667,1.6666666666666667,1.6833333333333333,1.5766666666666669,1.6433333333333333,1.6666666666666667,1.6900000000000002,1.6966666666666665,1.6633333333333333,1.7,1.72 +CHST1,1.7766666666666666,1.7533333333333332,1.79,1.7333333333333334,1.7466666666666668,1.76,1.7866666666666668,1.7466666666666668,1.7733333333333334,1.74,1.7733333333333334,1.75,1.7066666666666668,1.75,1.72,1.72,1.75,1.78,1.7433333333333334,1.72,1.7533333333333332,1.7233333333333334,1.7033333333333334,1.7300000000000002,1.78,1.6733333333333331,1.75,1.7433333333333334,1.76,1.8099999999999998,1.82,1.7633333333333334,1.75 +CHST10,1.5733333333333333,1.55,1.5533333333333335,1.5733333333333333,1.55,1.5933333333333335,1.71,1.5599999999999998,1.6466666666666667,1.6466666666666667,1.62,1.6266666666666667,1.6266666666666667,1.6266666666666667,1.61,1.6266666666666667,1.6633333333333333,1.67,1.6866666666666665,1.6600000000000001,1.6133333333333333,1.6366666666666667,1.6600000000000001,1.6533333333333333,1.7733333333333334,1.7966666666666666,1.7766666666666666,1.7766666666666666,1.8066666666666666,1.7733333333333334,1.7966666666666666,1.7733333333333334,1.7633333333333334 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+CHST15,1.265,1.2383333333333333,1.2616666666666667,1.3733333333333333,1.385,1.3466666666666667,1.3283333333333334,1.39,1.3216666666666665,1.4483333333333333,1.415,1.415,1.3783333333333332,1.3399999999999999,1.3533333333333333,1.4416666666666667,1.4233333333333331,1.3683333333333334,1.31,1.2983333333333333,1.3233333333333335,1.415,1.3816666666666666,1.3633333333333333,1.1916666666666667,1.02,1.0583333333333333,1.2166666666666666,1.1533333333333333,1.155,1.1783333333333335,1.185,1.2016666666666667 +CHST2,1.3025,1.2825,1.31,1.3725,1.32,1.3075,1.3525,1.2475,1.2825,1.3025,1.3075,1.27,1.3175,1.3025,1.2875,1.28,1.285,1.3375,1.325,1.3425,1.315,1.3575,1.36,1.37,1.5425,1.725,1.7075,1.6725,1.63,1.625,1.54,1.46,1.55 +CHST3,1.5833333333333333,1.6333333333333335,1.6600000000000001,2.1533333333333333,2.03,2.1766666666666667,1.6633333333333333,1.6900000000000002,1.5933333333333335,1.9433333333333334,1.8499999999999999,2.4433333333333334,1.9033333333333333,1.7366666666666666,1.6066666666666667,2.02,1.8433333333333335,1.8966666666666667,1.58,1.4666666666666668,1.4566666666666668,1.5733333333333333,1.6533333333333333,1.5233333333333334,2.0,2.4133333333333336,2.2133333333333334,2.0100000000000002,1.9733333333333334,1.9100000000000001,2.0233333333333334,2.13,1.9800000000000002 +CHST4,5.3025,5.4825,5.2825,5.9174999999999995,5.282500000000001,5.817500000000001,5.54,5.56,5.397499999999999,5.2375,4.9350000000000005,5.077500000000001,5.432499999999999,5.390000000000001,5.1775,4.9875,5.5874999999999995,5.43,5.6,5.41,5.365,5.2025,5.3575,5.2875,5.6225000000000005,5.245,4.86,5.385,5.6325,5.375,5.665,5.3625,5.3575 +CHST5,1.0275,0.98,1.01,0.9975,1.0225,0.9475,0.98,0.9525,0.985,0.99,0.99,0.98,0.97,1.0125,0.9575,0.9525,0.9475,0.94,0.9675,1.0025,0.9425,0.98,0.9575,1.015,1.0425,0.9475,0.9775,0.995,1.0375,0.975,0.9775,0.98,1.025 +CHST6,8.01,8.03,8.06,7.4875,7.5925,7.695,7.58,7.8375,7.7525,7.7575,7.952500000000001,7.6975,7.380000000000001,7.5024999999999995,7.7,7.69,7.585,7.455,7.63,7.6925,7.895,7.422499999999999,7.63,7.5275,7.550000000000001,7.19,7.1725,7.0725,7.42,7.305,7.1025,7.41,7.3 +CHST7,7.78,8.0825,7.8525,8.262500000000001,8.0225,8.209999999999999,8.07,7.8625,7.9575000000000005,7.9350000000000005,7.8325000000000005,7.8525,8.350000000000001,8.2,8.1875,8.1225,7.9625,8.08,7.8975,7.9825,8.287500000000001,8.377500000000001,8.2275,8.155000000000001,7.6775,8.02,8.0075,8.344999999999999,7.74,8.145,7.7525,7.702500000000001,7.81 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+CLECL1P,1.3166666666666667,1.37,1.2766666666666666,1.3499999999999999,1.55,1.4266666666666667,1.3366666666666667,1.24,1.37,1.3633333333333333,1.39,1.2766666666666666,1.4000000000000001,1.37,1.31,1.2933333333333332,1.4533333333333334,1.3666666666666665,1.3833333333333335,1.3766666666666667,1.3666666666666665,1.3833333333333335,1.33,1.39,1.4533333333333334,1.37,1.3866666666666667,1.4766666666666666,1.3666666666666665,1.23,1.3666666666666665,1.36,1.39 +CLGN,5.976666666666667,6.133333333333334,5.883333333333334,6.07,6.116666666666666,6.076666666666667,6.193333333333333,6.156666666666666,6.156666666666667,5.976666666666667,5.9399999999999995,5.793333333333333,6.069999999999999,6.09,5.973333333333334,5.863333333333333,6.06,5.976666666666667,6.073333333333333,6.383333333333334,6.016666666666667,6.013333333333334,5.703333333333333,5.986666666666667,8.003333333333334,6.98,6.22,6.23,6.0600000000000005,6.09,6.11,6.066666666666667,6.173333333333333 +CLHC1,1.6675,1.7225,1.6975,1.7625,1.7875,1.7925,1.7175,1.69,1.685,1.8025,1.8075,1.7525,1.67,1.665,1.67,1.7225,1.7975,1.7625,1.6675,1.6575,1.6575,1.65,1.655,1.6575,1.6475,1.6625,1.6825,1.6425,1.5775,1.6575,1.6925,1.6775,1.69 +CLIC1,13.524583333333332,13.828333333333333,13.811250000000001,14.02625,13.67875,13.765,13.5075,13.722916666666666,13.654583333333333,13.437916666666666,13.22625,13.598333333333333,13.87125,14.085833333333333,13.9475,13.488333333333333,13.514583333333333,13.725833333333334,13.957083333333333,14.086666666666666,14.101666666666667,13.752916666666666,13.832916666666668,13.712083333333334,13.514583333333334,13.564583333333333,13.513333333333334,13.6775,13.48375,13.518333333333334,12.653749999999999,12.8125,12.696666666666665 +CLIC2,1.54,1.4800000000000002,1.5433333333333332,1.54,1.5166666666666666,1.5133333333333334,1.55,1.55,1.58,1.4833333333333334,1.5,1.5033333333333332,1.4833333333333334,1.5199999999999998,1.55,1.5466666666666666,1.5266666666666666,1.5466666666666666,1.5533333333333335,1.5166666666666666,1.61,1.5433333333333332,1.57,1.55,1.5933333333333335,1.6266666666666667,1.54,1.67,1.5999999999999999,1.7133333333333332,1.6133333333333333,1.5733333333333333,1.6366666666666667 +CLIC3,1.6533333333333333,2.0,2.0166666666666666,2.6233333333333335,2.86,2.6999999999999997,1.7466666666666668,2.21,1.9000000000000001,2.8066666666666666,2.5966666666666667,2.796666666666667,2.0933333333333333,1.9400000000000002,1.96,3.0,2.7100000000000004,2.723333333333333,2.0166666666666666,2.0500000000000003,2.0166666666666666,2.06,2.393333333333333,2.4466666666666668,1.6066666666666667,1.6166666666666665,1.63,1.6666666666666667,1.6533333333333333,1.6766666666666667,1.6666666666666667,1.63,1.6766666666666667 +CLIC4,13.164666666666665,13.244666666666665,13.313083333333333,13.217666666666666,13.224416666666666,13.399583333333332,12.860499999999998,12.962333333333333,12.646333333333333,13.271416666666667,13.238750000000001,13.296583333333334,13.153833333333335,13.109666666666666,12.979583333333332,13.549,13.491666666666667,13.125666666666667,13.099,13.347333333333333,13.12225,13.104416666666665,13.24325,13.199083333333334,13.336166666666667,13.659166666666666,13.488666666666667,13.635916666666667,13.479083333333334,13.382083333333334,13.104416666666667,12.891583333333333,13.127166666666668 +CLIC5,1.5633333333333335,1.5666666666666667,1.5899999999999999,1.7766666666666666,1.7766666666666666,1.8333333333333333,1.5866666666666667,1.5999999999999999,1.57,2.356666666666667,2.25,2.0433333333333334,1.6833333333333333,1.7233333333333334,1.6133333333333333,2.1733333333333333,2.09,1.89,1.5833333333333333,1.6466666666666667,1.5733333333333333,1.61,1.6500000000000001,1.67,1.5466666666666666,1.49,1.51,1.5766666666666669,1.5933333333333335,1.5999999999999999,1.6600000000000001,1.6500000000000001,1.6600000000000001 +CLIC6,6.065,6.045,6.05,6.205,5.86,6.17,6.234999999999999,5.805,6.21,5.91,6.475,6.01,6.1850000000000005,6.27,6.135,6.175,6.105,6.16,6.445,6.49,6.325,6.175,6.3,6.21,7.405,6.82,6.859999999999999,6.555,6.36,6.585,7.18,7.38,7.195 +CLINT1,3.80375,3.847083333333333,3.9295833333333334,3.9425,3.955,4.044166666666667,3.748333333333333,3.7816666666666667,3.7179166666666665,3.856666666666667,3.875,3.745,3.7108333333333334,3.6950000000000003,3.7104166666666667,3.7770833333333336,3.8216666666666668,3.72375,3.764166666666666,3.8487500000000003,3.767916666666667,3.71875,3.7325,3.8083333333333336,4.061249999999999,3.92875,3.9533333333333336,3.8325,3.825833333333333,3.8379166666666666,3.962083333333333,3.9362500000000002,3.9191666666666665 +CLIP1,2.178,2.164,2.166,2.3760000000000003,2.384,2.356,2.262,2.226,2.1879999999999997,2.394,2.466,2.452,2.3120000000000003,2.182,2.196,2.428,2.4059999999999997,2.334,2.2079999999999997,2.17,2.198,2.2399999999999998,2.21,2.21,2.2359999999999998,2.192,2.174,2.182,2.176,2.132,2.304,2.31,2.272 +CLIP2,7.3725,7.2125,7.38,7.33,7.58,7.715,7.605,7.32,7.2675,7.375,7.2025,6.777500000000001,7.26,7.115,7.050000000000001,7.215,7.0925,7.5275,7.2775,7.220000000000001,7.3775,7.352499999999999,7.495,7.525,7.475,7.452500000000001,7.3025,7.6000000000000005,7.835,7.6025,7.7125,7.665,7.925 +CLIP3,1.5933333333333335,1.5933333333333335,1.5899999999999999,1.5999999999999999,1.5666666666666667,1.63,1.5933333333333335,1.57,1.61,1.6666666666666667,1.6833333333333333,1.5833333333333333,1.5333333333333332,1.5899999999999999,1.57,1.5933333333333335,1.62,1.5599999999999998,1.5766666666666669,1.5866666666666667,1.54,1.5533333333333335,1.5866666666666667,1.5899999999999999,1.5933333333333335,1.5533333333333335,1.5933333333333335,1.5899999999999999,1.5933333333333335,1.5933333333333335,1.6366666666666667,1.6333333333333335,1.64 +CLIP4,1.442,1.5,1.456,1.738,1.8059999999999998,1.714,1.542,1.522,1.4300000000000002,1.938,2.008,1.746,1.572,1.534,1.498,1.936,1.988,1.7899999999999998,1.516,1.53,1.548,1.686,1.67,1.658,1.658,1.584,1.6320000000000001,1.6260000000000001,1.518,1.55,1.848,1.7920000000000003,1.786 +CLK1,2.0875,2.11,2.21,2.495,2.4375,2.645,2.2375,2.1175,2.1525,2.4525,2.3925,2.3975,2.24,2.3,2.23,2.3425,2.5425,2.395,2.17,2.2375,2.3075,2.32,2.2,2.2075,2.1325,2.3275,2.3125,2.2,2.21,2.245,2.265,2.1775,2.165 +CLK2,1.8739999999999999,1.8739999999999999,1.9280000000000002,1.7760000000000002,1.916,1.8780000000000001,1.83,1.918,1.8679999999999999,1.9260000000000002,1.9420000000000002,1.848,1.906,1.916,1.916,1.8699999999999999,1.952,1.908,1.832,1.86,1.916,1.8780000000000001,1.826,1.848,1.9120000000000001,1.9739999999999998,1.98,1.92,1.9760000000000002,1.9600000000000002,1.938,1.98,1.952 +CLK2P1,3.195,3.04,3.295,2.95,3.17,3.005,3.205,3.04,2.945,3.07,2.835,2.91,3.215,3.225,3.355,3.22,3.25,3.09,3.065,3.02,3.255,3.225,3.035,3.185,3.16,3.6,3.375,3.44,3.42,3.425,3.29,3.41,3.445 +CLK3,2.3876190476190478,2.4116666666666666,2.472142857142857,2.439761904761905,2.4883333333333333,2.459047619047619,2.3192857142857144,2.4,2.3376190476190475,2.4366666666666665,2.5183333333333335,2.327142857142857,2.376666666666667,2.4302380952380953,2.394285714285714,2.487142857142857,2.4076190476190478,2.3366666666666664,2.4116666666666666,2.4135714285714283,2.448571428571429,2.428095238095238,2.4321428571428574,2.453095238095238,2.41,2.3878571428571425,2.405952380952381,2.385238095238095,2.2752380952380955,2.3552380952380956,2.325,2.380952380952381,2.36952380952381 +CLK4,6.288,6.274,5.9319999999999995,6.343999999999999,6.558,6.18,6.604,6.188,6.16,6.268,6.3919999999999995,6.376,6.168,6.324,6.481999999999999,6.056,6.327999999999999,6.406000000000001,6.26,6.146,6.460000000000001,6.234,6.19,6.284000000000001,6.34,6.024000000000001,6.146000000000001,6.41,6.104,6.312,6.396,6.508,6.372 +CLLU1,1.94,1.94,1.83,1.92,1.945,2.07,2.02,2.08,1.92,2.125,2.02,2.09,1.935,1.935,2.09,1.92,1.945,1.86,1.945,1.835,2.015,1.87,1.945,1.945,1.885,1.975,1.955,1.98,1.83,1.94,1.9,1.89,1.885 +CLLU1-AS1,1.985,2.16,2.045,2.06,2.07,2.16,2.17,2.075,2.18,1.935,2.08,2.02,2.12,2.1,2.07,2.035,2.18,2.06,1.895,2.135,2.06,2.055,2.07,1.97,2.065,1.855,1.935,1.895,2.09,2.015,2.095,2.11,2.04 +CLMN,4.305,4.555,4.45,4.35,4.425,4.335,3.505,3.865,3.73,3.88,4.08,3.76,3.375,3.615,3.555,3.845,3.8,3.725,3.9,3.86,3.865,3.775,3.96,3.96,3.87,3.8,3.72,3.32,3.29,3.335,3.435,3.365,3.295 +CLMP,1.5333333333333332,1.5899999999999999,1.4866666666666666,1.5833333333333333,1.5233333333333334,1.5766666666666669,1.5899999999999999,1.6033333333333333,1.5533333333333335,1.54,1.5,1.6266666666666667,1.5333333333333332,1.5333333333333332,1.5066666666666666,1.4366666666666665,1.5266666666666666,1.5333333333333332,1.5433333333333332,1.4933333333333334,1.5333333333333332,1.53,1.5333333333333332,1.5533333333333335,1.54,1.5233333333333334,1.4766666666666666,1.5333333333333332,1.5666666666666667,1.5366666666666668,1.5266666666666666,1.5166666666666666,1.5433333333333332 +CLN3,1.9825,2.0275,2.04,2.3625,2.265,2.3525,1.9575,1.965,1.9875,1.9175,1.9825,1.9125,1.9475,1.9925,1.9825,1.9225,1.935,1.925,1.985,2.0675,2.0275,1.895,1.9675,2.1775,2.145,2.1225,2.1825,2.11,2.015,2.0,2.2925,2.315,2.27 +CLN5,3.5,3.555,3.57,3.85,3.74,3.875,3.5,3.485,3.445,3.555,3.59,3.48,3.59,3.59,3.565,3.675,3.685,3.655,3.57,3.57,3.7,3.505,3.6,3.58,3.57,3.65,3.605,3.495,3.425,3.535,3.515,3.545,3.485 +CLN6,2.7825,2.755,2.78,2.265,2.2575,2.23,2.6675,2.74,2.7675,2.2875,2.26,2.7125,2.725,2.565,2.64,2.37,2.235,2.405,2.705,2.555,2.685,2.7775,2.6525,2.5275,2.4825,2.725,2.635,2.6725,2.7725,2.66,2.6425,2.6975,2.6425 +CLN8,1.5325,1.5625,1.53,1.535,1.56,1.555,1.565,1.5325,1.5325,1.545,1.5,1.555,1.6175,1.6,1.57,1.5675,1.53,1.5475,1.59,1.6075,1.595,1.5875,1.595,1.58,1.76,1.6675,1.6725,1.6175,1.6125,1.5875,1.665,1.6175,1.6 +CLNK,1.4400000000000002,1.43,1.4333333333333333,1.4466666666666665,1.4333333333333333,1.5,1.45,1.43,1.4633333333333332,1.43,1.4466666666666665,1.4466666666666665,1.46,1.3966666666666667,1.4466666666666665,1.4166666666666667,1.4166666666666667,1.4333333333333333,1.4566666666666668,1.47,1.4366666666666665,1.4533333333333334,1.4466666666666665,1.4466666666666665,1.4466666666666665,1.4166666666666667,1.4466666666666665,1.4466666666666665,1.4466666666666665,1.4633333333333332,1.4666666666666668,1.47,1.4866666666666666 +CLNS1A,4.8,4.705,4.7,4.595,4.62,4.58,4.765,4.73,4.67,4.48,4.32,4.265,4.865,4.97,4.935,4.76,4.68,4.79,4.83,4.875,4.955,4.905,4.82,4.78,4.595,5.08,4.825,4.845,4.93,5.03,4.76,4.875,4.845 +CLOCK,2.52,2.52,2.52,2.37,2.4125,2.3625,2.41,2.54,2.4825,2.2475,2.255,2.2625,2.4325,2.42,2.385,2.3975,2.325,2.3825,2.6125,2.6125,2.6525,2.7225,2.6875,2.6375,2.5975,2.5275,2.5075,2.48,2.49,2.5675,2.385,2.4425,2.3925 +CLP1,6.885,7.035,6.985,7.125,7.125,7.1850000000000005,6.99,7.305,7.325,6.734999999999999,6.9350000000000005,6.84,7.470000000000001,7.08,7.244999999999999,7.13,7.285,7.115,7.35,6.98,7.43,7.155,7.285,6.9350000000000005,6.904999999999999,7.015000000000001,7.08,7.205,7.154999999999999,6.859999999999999,6.65,6.885,7.245 +CLPB,8.31,7.85,8.165,7.324999999999999,6.869999999999999,6.975,7.654999999999999,8.695,7.970000000000001,7.275,6.925,7.03,8.115,8.575,8.55,7.52,7.01,7.595000000000001,8.18,8.14,8.67,7.879999999999999,8.165,7.96,7.21,7.66,7.61,7.54,7.525,6.970000000000001,7.035,7.15,7.045 +CLPP,3.5533333333333332,3.5766666666666667,3.59,3.393333333333333,3.4266666666666663,3.44,3.606666666666667,3.86,3.736666666666667,3.4633333333333334,3.436666666666667,3.6566666666666667,4.14,3.99,4.0,3.6300000000000003,3.376666666666667,3.6566666666666667,3.733333333333333,3.716666666666667,3.763333333333333,3.6733333333333333,3.5966666666666662,3.473333333333333,3.66,3.796666666666667,3.606666666666667,3.686666666666667,3.6799999999999997,3.5500000000000003,3.4,3.5433333333333334,3.393333333333333 +CLPS,1.27,1.2725,1.305,1.225,1.2,1.2375,1.24,1.2725,1.305,1.245,1.3125,1.2825,1.195,1.185,1.2125,1.1675,1.1775,1.23,1.2025,1.21,1.23,1.23,1.1825,1.2375,1.2025,1.2125,1.225,1.195,1.225,1.225,1.26,1.235,1.3125 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+CNOT9,2.128,2.1,2.1260000000000003,1.9120000000000001,1.8940000000000001,1.862,2.1,2.092,2.116,1.902,1.844,2.038,2.1399999999999997,2.13,2.148,2.0,1.966,2.044,2.128,2.112,2.158,2.168,2.1239999999999997,2.092,2.158,2.164,2.102,2.132,2.21,2.148,2.0,2.072,2.018 +CNP,1.3733333333333333,1.4033333333333333,1.4266666666666667,1.4333333333333333,1.4366666666666665,1.4366666666666665,1.4000000000000001,1.4433333333333334,1.3366666666666667,1.3933333333333333,1.3866666666666667,1.4316666666666666,1.4283333333333335,1.3716666666666668,1.4366666666666665,1.45,1.365,1.4216666666666666,1.4000000000000001,1.3949999999999998,1.4183333333333332,1.43,1.405,1.4000000000000001,1.4766666666666666,1.5599999999999998,1.515,1.55,1.5899999999999999,1.5183333333333333,1.5266666666666666,1.5483333333333331,1.505 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+CNRIP1,1.3,1.2966666666666666,1.31,1.3233333333333335,1.31,1.3,1.3333333333333333,1.3133333333333332,1.3633333333333333,1.5033333333333332,1.4633333333333332,1.5599999999999998,1.3466666666666667,1.32,1.3333333333333333,1.43,1.4033333333333333,1.4366666666666665,1.3333333333333333,1.2833333333333334,1.2833333333333334,1.29,1.3333333333333333,1.3033333333333335,1.32,1.2933333333333332,1.3233333333333335,1.2833333333333334,1.3433333333333335,1.2933333333333332,1.36,1.31,1.33 +CNST,2.46,2.48,2.4466666666666668,2.4933333333333336,2.44,2.44,2.4433333333333334,2.4566666666666666,2.4366666666666665,2.3333333333333335,2.4499999999999997,2.3266666666666667,2.4766666666666666,2.58,2.526666666666667,2.5033333333333334,2.433333333333333,2.3666666666666667,2.48,2.4933333333333336,2.526666666666667,2.5233333333333334,2.48,2.4766666666666666,2.6133333333333333,2.9433333333333334,2.973333333333333,2.94,2.9600000000000004,2.983333333333333,2.8366666666666664,2.9333333333333336,2.8366666666666664 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+CNTN1,6.62,6.734999999999999,6.5,7.05,7.015000000000001,7.01,7.395,7.125,7.355,7.09,7.1,7.145,7.390000000000001,7.41,7.355,7.23,7.335,7.35,7.18,7.4350000000000005,7.125,7.065,7.18,7.13,6.9350000000000005,6.630000000000001,6.709999999999999,7.06,7.03,7.05,7.745,7.33,7.484999999999999 +CNTN2,1.4433333333333334,1.4366666666666665,1.4466666666666665,1.4333333333333333,1.3866666666666667,1.4066666666666665,1.4533333333333334,1.3966666666666667,1.51,1.4100000000000001,1.4266666666666667,1.42,1.4266666666666667,1.4366666666666665,1.4333333333333333,1.39,1.4400000000000002,1.4366666666666665,1.4400000000000002,1.4400000000000002,1.4233333333333331,1.4366666666666665,1.4400000000000002,1.4633333333333332,1.4766666666666666,1.3866666666666667,1.4466666666666665,1.4366666666666665,1.4400000000000002,1.4433333333333334,1.47,1.4766666666666666,1.4733333333333334 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+DNAAF9,2.7933333333333334,2.8633333333333333,2.9133333333333336,2.9066666666666667,3.0533333333333332,3.0,2.9933333333333336,2.8466666666666662,2.9066666666666667,2.953333333333333,2.8466666666666662,3.126666666666667,2.8466666666666662,2.8033333333333332,2.7866666666666666,2.8200000000000003,2.8666666666666667,2.8466666666666662,2.6799999999999997,2.81,2.723333333333333,2.89,2.766666666666667,2.8466666666666662,2.3466666666666667,2.6833333333333336,2.6333333333333333,2.8333333333333335,2.8366666666666664,2.8033333333333332,2.983333333333333,2.9133333333333336,2.9 +DNAH1,2.415,2.4,2.4,2.4,2.39,2.4,2.435,2.36,2.44,2.385,2.405,2.4,2.33,2.36,2.38,2.36,2.4,2.395,2.4,2.39,2.33,2.37,2.39,2.4,2.495,2.38,2.4,2.42,2.415,2.415,2.495,2.445,2.48 +DNAH10,2.205,2.195,2.185,2.215,2.19,2.205,2.285,2.195,2.215,2.205,2.205,2.185,2.2,2.2,2.195,2.145,2.2,2.185,2.22,2.205,2.14,2.205,2.205,2.265,2.315,2.175,2.195,2.22,2.25,2.21,2.29,2.245,2.26 +DNAH10OS,2.09,2.04,2.045,2.145,2.07,2.18,1.93,2.045,2.085,2.025,2.055,2.135,1.855,1.99,2.045,2.06,2.05,1.68,2.065,2.2,1.85,2.005,2.055,1.93,2.315,2.06,2.245,2.205,2.15,2.215,2.065,1.99,2.085 +DNAH11,2.145,2.17,2.17,2.225,2.15,2.19,2.27,2.21,2.27,3.13,2.925,3.37,2.335,2.27,2.205,2.725,2.655,2.515,2.235,2.2,2.115,2.23,2.265,2.23,2.255,2.165,2.155,2.24,2.255,2.22,2.275,2.23,2.26 +DNAH12,2.125,2.125,2.065,2.125,2.145,2.11,2.17,2.135,2.125,2.125,2.1,2.125,2.125,2.14,2.125,2.075,2.075,2.13,2.17,2.155,2.1,2.105,2.125,2.165,2.125,2.115,2.065,2.14,2.13,2.125,2.135,2.11,2.125 +DNAH14,6.16,6.295,6.245,6.390000000000001,6.0,6.12,6.275,5.915,6.585,6.41,6.255000000000001,6.154999999999999,5.904999999999999,6.225,6.145,5.904999999999999,5.91,6.13,6.26,6.07,6.24,6.2,6.244999999999999,6.02,7.295,6.705,6.695,6.65,6.71,6.6850000000000005,6.705,6.8100000000000005,6.765 +DNAH17,6.390000000000001,6.59,6.5,6.8100000000000005,6.58,6.73,7.15,6.765,6.76,6.495,6.445,6.555,7.545,6.785,6.935,6.59,6.6499999999999995,6.46,6.5600000000000005,6.77,6.734999999999999,6.93,6.4399999999999995,7.055,6.6,6.545,6.475,6.620000000000001,6.8100000000000005,6.655,6.705,6.765000000000001,6.49 +DNAH2,4.76,4.6,4.72,4.49,4.5,4.59,4.68,4.62,4.6,4.53,4.54,4.46,4.46,4.6,4.54,4.39,4.43,4.49,4.69,4.6,4.6,4.57,4.6,4.6,4.93,4.66,4.78,4.61,4.72,4.66,4.67,4.6,4.68 +DNAH3,2.16,2.145,2.145,2.175,2.16,2.165,2.225,2.19,2.23,2.195,2.16,2.16,2.16,2.16,2.16,2.09,2.16,2.16,2.165,2.16,2.14,2.16,2.18,2.2,2.16,2.09,2.11,2.14,2.16,2.16,2.22,2.145,2.18 +DNAH5,2.56,2.635,2.59,2.65,2.685,2.815,2.455,2.505,2.47,2.35,2.355,2.42,2.44,2.47,2.505,2.385,2.38,2.37,2.505,2.595,2.535,2.515,2.515,2.715,2.48,2.47,2.495,2.505,2.475,2.56,2.495,2.495,2.54 +DNAH6,2.065,2.065,2.065,2.095,2.065,2.12,2.09,2.025,2.11,2.065,2.065,2.065,2.06,2.065,2.065,2.035,2.065,2.025,2.115,2.085,2.065,2.075,2.075,2.065,2.14,2.02,2.045,2.04,2.065,2.065,2.065,2.06,2.065 +DNAH7,2.21,2.215,2.165,2.215,2.215,2.215,2.26,2.215,2.27,2.22,2.215,2.2,2.215,2.215,2.175,2.215,2.245,2.26,2.215,2.22,2.215,2.165,2.27,2.22,2.215,2.155,2.165,2.185,2.215,2.17,2.255,2.215,2.295 +DNAH8,2.04,2.065,2.005,2.105,2.045,2.065,2.14,2.045,2.12,2.065,2.065,2.065,2.065,2.065,2.05,1.995,2.065,2.075,2.075,2.09,2.065,2.065,2.11,2.08,2.11,2.0,2.065,2.065,2.075,2.065,2.105,2.065,2.065 +DNAH9,2.23,2.215,2.215,2.21,2.205,2.22,2.215,2.175,2.26,2.2,2.215,2.215,2.17,2.21,2.19,2.12,2.215,2.215,2.23,2.225,2.19,2.21,2.215,2.22,2.295,2.125,2.19,2.215,2.255,2.235,2.275,2.225,2.255 +DNAI1,1.5566666666666666,1.5533333333333335,1.53,1.5633333333333335,1.5599999999999998,1.5599999999999998,1.5966666666666667,1.5466666666666666,1.5833333333333333,1.5466666666666666,1.5166666666666666,1.5466666666666666,1.5599999999999998,1.5566666666666666,1.4866666666666666,1.5199999999999998,1.5633333333333335,1.5833333333333333,1.5566666666666666,1.5599999999999998,1.5133333333333334,1.5566666666666666,1.5666666666666667,1.6266666666666667,1.62,1.5199999999999998,1.4833333333333334,1.5866666666666667,1.5833333333333333,1.5766666666666669,1.63,1.5633333333333335,1.6366666666666667 +DNAI2,1.4266666666666667,1.3766666666666667,1.3966666666666667,1.4366666666666665,1.39,1.4400000000000002,1.4833333333333334,1.3766666666666667,1.4266666666666667,1.3666666666666665,1.4033333333333333,1.4166666666666667,1.4033333333333333,1.3866666666666667,1.4133333333333333,1.37,1.38,1.4333333333333333,1.4533333333333334,1.4233333333333331,1.4233333333333331,1.4033333333333333,1.4566666666666668,1.4633333333333332,1.4466666666666665,1.4266666666666667,1.42,1.4866666666666666,1.5033333333333332,1.5033333333333332,1.51,1.5,1.4966666666666668 +DNAI3,5.8975,6.117500000000001,5.895,6.0625,5.75,5.8475,6.0649999999999995,5.8475,5.7725,5.905,6.115,5.710000000000001,5.805,5.8675,5.9875,5.765,6.2775,5.9225,5.695,5.805,5.762499999999999,5.75,5.8825,6.257499999999999,5.8225,5.43,5.7775,5.96,5.7925,5.6525,6.1225,5.8875,6.245 +DNAI4,1.2925,1.2925,1.26,1.33,1.315,1.365,1.21,1.235,1.24,1.345,1.3925,1.2625,1.23,1.2175,1.22,1.3675,1.37,1.2925,1.255,1.2825,1.275,1.235,1.2725,1.2925,1.3025,1.2925,1.2925,1.315,1.2825,1.2875,1.4525,1.41,1.345 +DNAI7,0.9019999999999999,0.866,0.8480000000000001,0.9259999999999999,0.952,0.9339999999999999,0.898,0.9019999999999999,0.916,0.9019999999999999,0.9019999999999999,0.8880000000000001,0.8560000000000001,0.914,0.892,0.8779999999999999,0.8960000000000001,0.9099999999999999,0.9400000000000001,0.914,0.9019999999999999,0.9,0.9199999999999999,0.9380000000000001,0.9279999999999999,0.8320000000000001,0.8640000000000001,0.9019999999999999,0.8939999999999999,0.9019999999999999,0.9019999999999999,0.9039999999999999,0.9279999999999999 +DNAJA1,4.743333333333333,4.673333333333333,4.756666666666667,4.3966666666666665,4.4366666666666665,4.506666666666667,4.68,4.64,4.69,4.406666666666667,4.3533333333333335,4.4799999999999995,4.726666666666667,4.703333333333333,4.72,4.586666666666667,4.633333333333334,4.696666666666666,4.666666666666667,4.57,4.75,4.8,4.773333333333333,4.576666666666667,4.613333333333333,4.473333333333334,4.403333333333333,4.546666666666667,4.503333333333333,4.456666666666666,4.6033333333333335,4.6066666666666665,4.6499999999999995 +DNAJA1P5,5.48,5.26,5.17,5.52,5.15,5.02,5.44,5.23,5.42,5.08,4.65,5.32,5.23,4.98,5.54,4.91,5.22,5.31,5.35,5.14,5.73,5.38,5.58,5.33,5.73,5.43,5.16,5.47,5.78,5.81,5.56,5.44,5.78 +DNAJA2,3.0525,3.0425,3.055,3.0875,3.065,3.0875,3.05,3.015,2.98,2.845,2.845,2.8475,3.14,3.1425,3.1325,3.065,2.9375,3.0,3.1425,3.1,3.1875,3.1425,3.1275,3.0375,3.045,3.005,2.9675,2.9225,2.9675,2.9125,2.875,2.945,2.88 +DNAJA3,2.066,2.02,2.054,1.674,1.67,1.6940000000000002,1.8940000000000001,1.97,1.8960000000000001,1.7100000000000002,1.766,1.7420000000000002,2.0140000000000002,1.9940000000000002,1.966,1.8,1.7280000000000002,1.8059999999999998,2.04,2.0140000000000002,2.086,2.054,1.9899999999999998,1.8719999999999999,1.752,1.8780000000000001,1.78,1.752,1.8579999999999999,1.78,1.6800000000000002,1.748,1.72 +DNAJA4,2.3875,2.4025,2.46,2.2825,2.3575,2.35,2.275,2.31,2.305,2.215,2.2725,2.19,2.27,2.3275,2.295,2.11,2.17,2.225,2.0925,2.13,2.25,2.0925,2.05,2.0225,2.325,2.34,2.3425,2.2625,2.2825,2.29,2.435,2.43,2.3975 +DNAJB1,3.526666666666667,3.546666666666667,3.6133333333333333,3.5,3.4600000000000004,3.473333333333333,3.8366666666666664,4.0,3.9566666666666666,3.813333333333333,3.8333333333333335,4.023333333333333,3.856666666666667,3.9466666666666668,3.9600000000000004,3.686666666666667,3.6833333333333336,3.7433333333333336,3.516666666666667,3.5966666666666662,3.7100000000000004,3.64,3.623333333333333,3.4566666666666666,3.6999999999999997,3.703333333333333,3.606666666666667,3.6999999999999997,3.5866666666666664,3.623333333333333,3.8800000000000003,3.7933333333333334,3.7866666666666666 +DNAJB11,11.896666666666667,11.936666666666667,11.87,12.193333333333333,12.123333333333333,11.896666666666667,12.426666666666666,12.256666666666666,12.206666666666667,12.11,12.126666666666665,12.833333333333334,13.146666666666667,13.166666666666666,12.94,12.84,12.476666666666667,12.803333333333333,12.19,12.59,12.753333333333334,11.853333333333333,12.336666666666668,12.06,13.216666666666667,12.8,12.856666666666667,12.84,12.913333333333334,12.603333333333332,12.39,12.18,12.406666666666668 +DNAJB12,3.233333333333333,3.2533333333333334,3.2933333333333334,3.2433333333333336,3.216666666666667,3.2733333333333334,2.97,3.22,3.0666666666666664,3.25,3.2866666666666666,3.236666666666667,3.16,3.1166666666666667,3.0833333333333335,3.2533333333333334,3.0933333333333333,3.0399999999999996,3.143333333333333,3.2133333333333334,3.2066666666666666,3.1,3.1,3.1566666666666667,2.98,3.0766666666666667,2.936666666666667,2.8666666666666667,2.9033333333333338,2.8066666666666666,2.8333333333333335,2.9,2.8033333333333332 +DNAJB13,4.3,4.21,3.97,4.25,4.21,4.17,4.25,4.11,4.46,4.1,4.23,4.15,4.08,4.22,4.18,4.23,4.4,4.26,4.23,4.29,4.23,4.19,4.34,4.29,4.3,4.15,4.23,4.27,4.23,4.26,4.28,4.26,4.3 +DNAJB14,2.606666666666667,2.7066666666666666,2.6733333333333333,2.74,2.7533333333333334,2.783333333333333,2.736666666666667,2.6933333333333334,2.6633333333333336,2.873333333333333,2.8666666666666667,2.783333333333333,2.78,2.8833333333333333,2.8000000000000003,2.9166666666666665,2.91,2.83,2.8666666666666667,2.9333333333333336,2.9,2.856666666666667,2.8633333333333333,2.8266666666666667,2.7433333333333336,2.783333333333333,2.7866666666666666,2.74,2.74,2.75,2.69,2.686666666666667,2.6633333333333336 +DNAJB2,1.5375,1.5925,1.5825,1.7375,1.74,1.73,1.645,1.6,1.63,1.7,1.735,1.7425,1.74,1.64,1.645,1.7325,1.7775,1.7475,1.62,1.63,1.5875,1.63,1.63,1.64,1.665,1.63,1.6325,1.5775,1.5825,1.5575,1.6525,1.6325,1.6175 +DNAJB3,1.47,1.6166666666666665,1.4400000000000002,1.5466666666666666,1.6166666666666665,1.5766666666666669,1.4933333333333334,1.5599999999999998,1.5366666666666668,1.4666666666666668,1.5633333333333335,1.4866666666666666,1.5166666666666666,1.5533333333333335,1.5733333333333333,1.53,1.64,1.4766666666666666,1.5133333333333334,1.4466666666666665,1.5033333333333332,1.5866666666666667,1.5466666666666666,1.6233333333333333,1.5966666666666667,1.5833333333333333,1.7033333333333334,1.6066666666666667,1.4933333333333334,1.6466666666666667,1.5633333333333335,1.6733333333333331,1.7466666666666668 +DNAJB4,4.365,4.59,4.498333333333333,4.883333333333333,4.755,4.618333333333333,4.715,4.428333333333334,4.818333333333333,4.83,4.626666666666667,4.873333333333333,4.625,4.616666666666667,4.616666666666667,4.658333333333333,4.9,4.856666666666667,4.2716666666666665,4.691666666666666,4.526666666666667,4.556666666666667,5.015000000000001,4.713333333333334,4.596666666666667,4.3133333333333335,4.431666666666667,4.53,4.623333333333333,4.651666666666667,5.096666666666667,4.696666666666666,4.91 +DNAJB5,2.505,2.525,2.545,2.525,2.62,2.54,2.58,2.58,2.61,2.58,2.565,2.8,2.685,2.6,2.6,2.675,2.675,2.71,2.535,2.53,2.56,2.63,2.575,2.585,2.555,2.505,2.51,2.54,2.565,2.49,2.61,2.58,2.605 +DNAJB6,2.185,2.23,2.2375,2.3625,2.3125,2.3475,2.1275,2.1775,2.095,2.15,2.1225,2.1425,2.18,2.1125,2.1725,2.175,2.15,2.165,2.17,2.2175,2.2225,2.25,2.2325,2.2825,2.125,2.1425,2.1675,2.1675,2.1325,2.1475,2.185,2.235,2.195 +DNAJB7,1.7699999999999998,1.7699999999999998,1.5366666666666668,1.7633333333333334,1.7133333333333332,1.6566666666666665,1.7833333333333332,1.7966666666666666,1.6233333333333333,1.8666666666666665,1.5533333333333335,1.54,1.8733333333333333,1.8833333333333335,1.7566666666666666,1.7666666666666666,1.7566666666666666,1.7866666666666668,1.9466666666666665,1.7433333333333334,1.8533333333333333,1.8266666666666669,1.8,1.7233333333333334,1.78,1.6033333333333333,1.6900000000000002,1.7,1.7166666666666668,1.7366666666666666,1.6066666666666667,1.5566666666666666,1.5433333333333332 +DNAJB8,2.7199999999999998,2.785,2.69,2.9016666666666664,2.7266666666666666,2.8466666666666667,2.9483333333333333,2.8558333333333334,2.89,2.848333333333333,2.8525,2.845833333333333,2.8725,2.8499999999999996,2.8308333333333335,2.8266666666666667,2.9325,2.8516666666666666,2.965,2.905,2.785,2.8833333333333333,2.94,2.8758333333333335,2.9075,2.8175,2.8066666666666666,2.859166666666667,2.8833333333333333,2.845833333333333,2.885,2.770833333333333,2.799166666666667 +DNAJB8-AS1,1.7699999999999998,1.79,1.74,1.8966666666666667,1.7866666666666668,1.8466666666666667,1.9333333333333333,1.8733333333333333,1.86,1.8633333333333333,1.8399999999999999,1.7933333333333332,1.89,1.88,1.8933333333333333,1.8366666666666667,1.95,1.8566666666666667,1.9400000000000002,1.92,1.8,1.9433333333333334,1.95,1.8833333333333335,1.86,1.83,1.8166666666666667,1.8866666666666667,1.8933333333333333,1.7933333333333332,1.8499999999999999,1.7833333333333332,1.8266666666666669 +DNAJB9,2.0766666666666667,2.0633333333333335,2.14,2.2466666666666666,2.21,2.2066666666666666,2.1633333333333336,2.09,2.03,2.1766666666666667,2.216666666666667,2.103333333333333,2.1666666666666665,2.1733333333333333,2.1766666666666667,2.216666666666667,2.22,2.216666666666667,2.14,2.1766666666666667,2.1766666666666667,2.183333333333333,2.18,2.16,2.28,2.1766666666666667,2.14,2.16,2.1766666666666667,2.1966666666666668,2.3033333333333332,2.25,2.2333333333333334 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+MMEL1,1.124,1.114,1.1,1.108,1.108,1.108,1.114,1.1,1.1,1.106,1.1099999999999999,1.098,1.0539999999999998,1.092,1.0939999999999999,1.076,1.1,1.1,1.1,1.084,1.0779999999999998,1.0699999999999998,1.1,1.1,1.1179999999999999,1.092,1.084,1.1,1.108,1.122,1.124,1.114,1.122 +MMGT1,2.6666666666666665,2.716666666666667,2.7100000000000004,2.6566666666666667,2.6366666666666667,2.5966666666666667,2.5366666666666666,2.6966666666666668,2.606666666666667,2.7866666666666666,2.7066666666666666,2.8466666666666662,2.723333333333333,2.72,2.6366666666666667,2.7133333333333334,2.65,2.65,2.64,2.5966666666666667,2.6766666666666663,2.6833333333333336,2.6666666666666665,2.606666666666667,2.6966666666666668,2.7566666666666664,2.6666666666666665,2.62,2.65,2.6933333333333334,2.513333333333333,2.5,2.53 +MMP1,2.8800000000000003,2.9566666666666666,3.3233333333333337,1.9933333333333334,1.9666666666666668,2.0433333333333334,2.34,2.17,2.4433333333333334,1.6933333333333334,1.7066666666666668,1.6633333333333333,2.2033333333333336,2.27,2.04,1.6266666666666667,1.7,1.7533333333333332,2.5500000000000003,2.8333333333333335,2.316666666666667,1.97,2.1133333333333333,1.9100000000000001,2.566666666666667,3.97,4.126666666666667,2.92,2.566666666666667,3.1133333333333333,1.8,1.71,1.6866666666666665 +MMP10,2.445,2.445,2.865,2.37,2.365,2.51,2.475,2.19,2.63,2.225,2.22,2.26,2.715,2.83,2.415,2.28,2.255,2.285,2.375,2.52,2.295,2.28,2.315,2.45,3.17,4.64,4.735,4.81,3.57,5.055,2.29,2.175,2.355 +MMP11,1.5266666666666666,1.5066666666666666,1.5066666666666666,1.51,1.5133333333333334,1.5133333333333334,1.5366666666666668,1.4733333333333334,1.5333333333333332,1.5133333333333334,1.5133333333333334,1.5133333333333334,1.5133333333333334,1.4833333333333334,1.5033333333333332,1.5133333333333334,1.5133333333333334,1.5366666666666668,1.5233333333333334,1.5,1.5133333333333334,1.5133333333333334,1.5133333333333334,1.5033333333333332,1.5199999999999998,1.4733333333333334,1.4966666666666668,1.5133333333333334,1.5199999999999998,1.5133333333333334,1.5133333333333334,1.5266666666666666,1.5133333333333334 +MMP12,1.4266666666666667,1.5266666666666666,1.7699999999999998,2.72,2.9266666666666663,3.1633333333333336,1.6133333333333333,1.5,1.6366666666666667,1.37,1.3533333333333333,1.4066666666666665,1.4033333333333333,1.7366666666666666,1.5666666666666667,2.4366666666666665,2.3133333333333335,2.12,1.6966666666666665,2.1866666666666665,1.5966666666666667,1.3933333333333333,1.5733333333333333,1.5666666666666667,1.5433333333333332,2.0066666666666664,1.88,1.7066666666666668,1.5599999999999998,1.9333333333333333,1.3633333333333333,1.36,1.3166666666666667 +MMP13,2.146666666666667,2.36,2.5733333333333333,3.2100000000000004,3.3800000000000003,3.5733333333333337,3.1,2.78,3.0966666666666662,1.7466666666666668,2.03,1.82,2.5,3.546666666666667,3.5233333333333334,2.0,1.8966666666666667,2.0366666666666666,2.2666666666666666,3.4499999999999997,2.4533333333333336,1.8466666666666667,2.1333333333333333,2.3066666666666666,2.2399999999999998,2.76,3.0500000000000003,2.6966666666666668,2.353333333333333,2.5433333333333334,1.6366666666666667,1.7633333333333334,1.6933333333333334 +MMP14,4.013333333333333,4.006666666666667,4.06,4.036666666666666,4.003333333333333,4.073333333333333,3.9,3.9299999999999997,3.75,4.016666666666667,4.066666666666666,4.096666666666667,3.7133333333333334,3.7933333333333334,3.8200000000000003,3.813333333333333,3.783333333333333,3.5966666666666662,3.7933333333333334,4.006666666666667,3.8200000000000003,3.8466666666666662,3.8000000000000003,3.98,3.466666666666667,3.953333333333333,3.8966666666666665,3.8966666666666665,3.8366666666666664,3.796666666666667,3.65,3.6766666666666663,3.5833333333333335 +MMP15,2.3733333333333335,2.46,2.486666666666667,2.2266666666666666,2.17,2.2133333333333334,2.1266666666666665,2.3833333333333333,2.236666666666667,1.9733333333333334,2.1266666666666665,1.9466666666666665,2.09,2.263333333333333,2.1866666666666665,2.08,1.9733333333333334,1.9933333333333334,2.23,2.28,2.2566666666666664,2.0766666666666667,2.23,2.2966666666666664,2.1,2.0366666666666666,1.9866666666666666,1.9833333333333334,1.9533333333333334,1.9966666666666668,1.9166666666666667,1.9433333333333334,1.9933333333333334 +MMP16,1.0075,1.0525,1.0075,1.0675,1.0575,1.085,1.03,1.0525,1.035,1.04,1.0525,1.0525,1.0525,1.07,1.03,1.0275,1.005,1.03,1.0525,1.115,1.0625,1.07,1.0975,1.0575,1.18,1.0475,1.0125,1.0525,1.0525,1.04,1.09,1.0475,1.045 +MMP17,1.5666666666666667,1.5199999999999998,1.5533333333333335,1.5166666666666666,1.53,1.5266666666666666,1.5966666666666667,1.5599999999999998,1.5566666666666666,1.5466666666666666,1.53,1.5433333333333332,1.5199999999999998,1.5433333333333332,1.5433333333333332,1.5266666666666666,1.54,1.5766666666666669,1.5266666666666666,1.5533333333333335,1.5433333333333332,1.5433333333333332,1.5466666666666666,1.5633333333333335,1.5599999999999998,1.4933333333333334,1.4833333333333334,1.53,1.5599999999999998,1.5433333333333332,1.5433333333333332,1.58,1.55 +MMP19,2.26,2.405,2.3,2.33,2.3,2.235,2.39,2.23,2.345,2.185,2.33,2.245,2.2,2.365,2.295,2.23,2.285,2.33,2.435,2.49,2.43,2.455,2.525,2.5,2.51,2.93,3.185,3.02,2.8,3.1,3.205,3.18,3.26 +MMP2,5.5875,5.18,5.2325,5.4575,5.5175,5.2275,5.735,5.702500000000001,5.7225,4.995,4.967499999999999,5.025,5.702500000000001,5.9375,5.88,5.3475,5.285,5.38,5.7475,5.5,5.57,5.56,5.615,5.3575,5.51,5.485,5.4799999999999995,6.1899999999999995,5.9825,6.03,5.515,5.2025,5.38 +MMP20,4.52,4.55,4.57,4.62,4.62,4.55,4.76,4.35,4.75,4.63,4.39,4.4,4.36,4.62,4.55,4.55,4.51,4.61,4.71,4.67,4.47,4.6,4.88,4.69,4.81,4.4,4.59,4.78,4.71,4.75,4.89,4.62,4.81 +MMP21,6.445,5.92,6.49,5.895,6.49,6.199999999999999,6.195,6.555,6.32,6.045,6.36,6.2,6.390000000000001,6.36,6.395,6.119999999999999,6.17,5.91,5.98,5.97,6.205,5.835,6.16,5.95,6.305,5.9,6.175,6.015,6.635,6.1850000000000005,6.78,6.045,6.38 +MMP23A,1.1425,1.1175,1.1175,1.1125,1.0975,1.1225,1.1,1.1,1.11,1.105,1.115,1.11,1.0975,1.0975,1.0975,1.07,1.1025,1.095,1.095,1.0975,1.0775,1.1,1.09,1.1,1.11,1.1,1.085,1.1075,1.11,1.1,1.1125,1.1,1.1 +MMP23B,1.1425,1.1175,1.1175,1.1125,1.0975,1.1225,1.1,1.1,1.11,1.105,1.115,1.11,1.0975,1.0975,1.0975,1.07,1.1025,1.095,1.095,1.0975,1.0775,1.1,1.09,1.1,1.11,1.1,1.085,1.1075,1.11,1.1,1.1125,1.1,1.1 +MMP24,4.6866666666666665,4.826666666666666,4.826666666666666,4.841666666666667,4.878333333333334,4.921666666666667,4.755,4.76,4.6883333333333335,5.013333333333334,5.046666666666667,4.8966666666666665,4.533333333333333,4.675000000000001,4.568333333333333,4.8116666666666665,4.691666666666666,4.748333333333333,4.598333333333334,4.631666666666667,4.57,4.525,4.568333333333333,4.781666666666666,4.5649999999999995,4.516666666666667,4.538333333333334,4.596666666666667,4.5633333333333335,4.518333333333333,4.683333333333334,4.6466666666666665,4.643333333333333 +MMP24OS,2.1766666666666667,2.3066666666666666,2.316666666666667,2.3366666666666664,2.3233333333333333,2.3666666666666667,2.19,2.2600000000000002,2.1733333333333333,2.5233333333333334,2.5366666666666666,2.3366666666666664,2.0633333333333335,2.18,2.1333333333333333,2.2866666666666666,2.2466666666666666,2.2033333333333336,2.0833333333333335,2.1766666666666667,2.11,2.04,2.0433333333333334,2.2466666666666666,2.02,2.0466666666666664,2.0433333333333334,2.0766666666666667,2.0233333333333334,2.013333333333333,2.1733333333333333,2.0966666666666667,2.103333333333333 +MMP25,4.58,4.58,4.67,4.57,4.58,4.53,4.58,4.48,4.59,4.66,4.63,4.57,4.46,4.49,4.52,4.47,4.58,4.58,4.58,4.57,4.46,4.48,4.55,4.49,4.61,4.56,4.53,4.63,4.64,4.61,4.79,4.58,4.69 +MMP26,0.975,0.9675,0.995,0.9975,0.99,0.985,1.0075,0.9675,1.085,1.0525,0.995,0.99,1.0,0.975,0.9625,0.98,0.9325,0.99,0.995,1.0475,0.9425,0.99,1.0025,0.9525,1.05,0.9625,0.945,0.985,0.995,0.995,1.005,0.99,1.0125 +MMP27,4.18,4.12,4.16,4.2,4.16,4.03,4.2,4.22,4.16,4.48,4.06,3.89,4.16,4.18,4.16,3.95,4.12,4.01,4.34,4.31,4.15,4.21,4.2,4.41,4.46,4.11,4.23,4.13,4.25,4.05,4.02,4.16,4.07 +MMP28,3.655,3.695,3.76,3.37,3.495,3.27,3.215,3.62,3.355,3.615,3.965,3.235,3.305,3.545,3.64,3.44,3.26,3.3,3.305,3.32,3.28,3.02,3.01,3.08,2.915,3.32,3.445,3.065,3.105,3.03,2.985,2.835,2.895 +MMP3,0.6142857142857142,0.6385714285714286,0.6228571428571429,0.6228571428571429,0.6414285714285715,0.6385714285714286,0.6414285714285715,0.6171428571428572,0.6442857142857142,0.62,0.6185714285714285,0.6214285714285713,0.6128571428571429,0.6271428571428571,0.6357142857142858,0.6171428571428572,0.62,0.63,0.6414285714285715,0.6742857142857143,0.6528571428571429,0.6171428571428572,0.6514285714285714,0.6671428571428571,0.6785714285714286,0.66,0.65,0.6485714285714286,0.6785714285714286,0.6414285714285715,0.67,0.6271428571428571,0.6428571428571429 +MMP7,1.4466666666666665,1.4866666666666666,1.4133333333333333,1.7300000000000002,1.6833333333333333,1.6266666666666667,1.5433333333333332,1.5,1.5233333333333334,1.6366666666666667,1.6900000000000002,1.4800000000000002,1.5466666666666666,1.54,1.5166666666666666,1.6366666666666667,1.5966666666666667,1.5599999999999998,1.5066666666666666,1.5866666666666667,1.57,1.5366666666666668,1.5033333333333332,1.6033333333333333,1.6900000000000002,2.3866666666666667,2.39,1.9566666666666668,1.7833333333333332,2.04,2.1866666666666665,2.14,2.1933333333333334 +MMP8,2.185,2.19,2.14,2.275,2.175,2.045,2.22,2.19,2.155,2.095,2.15,2.145,2.09,2.145,2.1,2.145,2.16,2.155,2.16,2.145,2.045,2.085,2.32,2.155,2.21,2.065,2.135,2.115,2.1,2.195,2.15,2.145,2.145 +MMP9,1.3625,1.3325,1.3325,1.335,1.3325,1.36,1.4075,1.2825,1.3325,1.335,1.3025,1.3325,1.355,1.3325,1.3325,1.2925,1.3125,1.325,1.3575,1.33,1.305,1.3325,1.345,1.3225,1.3475,1.28,1.2975,1.3325,1.355,1.345,1.3425,1.3325,1.345 +MMRN1,1.3566666666666667,1.38,1.3466666666666667,1.4000000000000001,1.3933333333333333,1.42,1.4833333333333334,1.3333333333333333,1.4400000000000002,1.4033333333333333,1.3866666666666667,1.3933333333333333,1.3366666666666667,1.3633333333333333,1.3266666666666667,1.36,1.3466666666666667,1.3566666666666667,1.4233333333333331,1.37,1.3666666666666665,1.3466666666666667,1.37,1.3733333333333333,1.36,1.36,1.39,1.3733333333333333,1.3733333333333333,1.3566666666666667,1.4166666666666667,1.3733333333333333,1.3733333333333333 +MMRN2,2.055,2.05,2.1,2.3275,2.3625,2.36,1.84,1.9225,1.8825,1.875,1.9775,1.8025,1.715,1.8625,1.91,1.86,1.8625,1.8225,2.005,2.0825,1.9475,1.735,1.845,2.035,1.6175,1.62,1.615,1.63,1.68,1.6275,1.655,1.645,1.61 +MMS19,2.075,2.095,2.1125,1.875,1.9725,1.93,2.0325,2.0825,2.0625,2.065,2.03,2.085,2.0525,1.9725,2.01,1.9725,1.9725,1.9825,1.995,1.95,1.975,2.0625,1.9825,1.925,1.76,1.9075,1.9125,1.9175,1.89,1.8575,1.83,1.8525,1.8125 +MMS22L,7.02,6.85,7.67,6.52,6.8100000000000005,6.773333333333333,7.133333333333333,6.74,7.536666666666667,6.343333333333333,6.1,7.173333333333334,6.993333333333334,7.030000000000001,6.9,6.303333333333333,6.29,6.7333333333333325,7.0633333333333335,7.460000000000001,7.053333333333334,6.98,7.163333333333334,6.793333333333333,6.926666666666668,6.883333333333333,6.486666666666666,6.796666666666667,6.886666666666667,6.953333333333333,6.51,6.5600000000000005,6.663333333333334 +MMUT,3.2933333333333334,3.4,3.4066666666666667,3.6333333333333333,3.563333333333333,3.546666666666667,3.22,3.2533333333333334,3.126666666666667,3.4166666666666665,3.3966666666666665,3.4033333333333338,3.1566666666666667,3.3166666666666664,3.3233333333333337,3.473333333333333,3.4600000000000004,3.4133333333333336,3.276666666666667,3.516666666666667,3.3866666666666667,3.15,3.25,3.49,3.5833333333333335,3.5733333333333337,3.623333333333333,3.53,3.5700000000000003,3.4933333333333336,3.4766666666666666,3.4133333333333336,3.36 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+RNF141,5.355,5.685,5.615,5.625,5.655,5.57,5.385,5.705,5.375,5.66,6.065,5.685,5.455,5.55,5.48,5.775,5.64,5.785,5.39,5.575,5.585,5.565,5.565,5.685,5.21,5.065,4.94,5.14,5.275,5.34,5.83,5.96,5.93 +RNF144A,2.8949999999999996,2.8425000000000002,2.8975,2.9158333333333335,2.8716666666666666,2.9450000000000003,2.9116666666666666,2.8341666666666665,2.9174999999999995,3.0283333333333333,3.045,3.095833333333333,2.8441666666666663,2.890833333333333,2.9141666666666666,3.01,2.970833333333333,2.8925,2.8875,2.9316666666666666,2.8275,2.895833333333333,3.02,2.910833333333333,3.1608333333333336,3.1783333333333337,3.128333333333333,3.1308333333333334,3.083333333333333,3.1508333333333334,3.13,3.16,3.1866666666666665 +RNF144B,2.6666666666666665,2.86,2.8000000000000003,3.2933333333333334,3.33,3.453333333333333,3.1300000000000003,3.373333333333333,3.16,3.8233333333333337,3.98,3.5933333333333333,2.9966666666666666,3.1633333333333336,3.126666666666667,3.3466666666666662,3.393333333333333,3.046666666666667,2.8333333333333335,3.02,2.8966666666666665,2.8966666666666665,3.03,3.2100000000000004,2.4566666666666666,3.1733333333333333,3.1833333333333336,3.233333333333333,2.9933333333333336,3.0733333333333337,3.6799999999999997,3.6199999999999997,3.5033333333333334 +RNF145,2.89,2.9966666666666666,3.046666666666667,2.6999999999999997,2.7433333333333336,2.6999999999999997,3.15,3.1999999999999997,3.0733333333333337,2.973333333333333,2.98,2.94,2.983333333333333,3.3366666666666664,3.2100000000000004,3.0666666666666664,3.0700000000000003,3.16,3.046666666666667,3.2566666666666664,3.1799999999999997,3.11,3.1966666666666668,3.16,2.9933333333333336,2.9600000000000004,3.03,3.1999999999999997,3.0500000000000003,3.106666666666667,2.81,2.8666666666666667,2.83 +RNF146,2.4066666666666667,2.433333333333333,2.4566666666666666,2.7100000000000004,2.686666666666667,2.6566666666666667,2.4833333333333334,2.48,2.4566666666666666,2.81,2.7733333333333334,2.5466666666666664,2.41,2.5066666666666664,2.5066666666666664,2.69,2.7433333333333336,2.733333333333333,2.4033333333333333,2.4899999999999998,2.52,2.5166666666666666,2.5,2.5566666666666666,2.5233333333333334,2.396666666666667,2.5166666666666666,2.39,2.283333333333333,2.38,2.53,2.5100000000000002,2.48 +RNF148,1.2766666666666666,1.3666666666666665,1.2933333333333332,1.3266666666666667,1.32,1.36,1.4833333333333334,1.39,1.49,1.3733333333333333,1.3533333333333333,1.38,1.3433333333333335,1.3666666666666665,1.3499999999999999,1.47,1.2933333333333332,1.3666666666666665,1.37,1.47,1.3033333333333335,1.2733333333333332,1.4433333333333334,1.36,1.57,1.37,1.32,1.3666666666666665,1.37,1.45,1.38,1.4266666666666667,1.4366666666666665 +RNF149,1.786,1.8379999999999999,1.816,1.8739999999999999,1.86,1.904,1.848,1.8239999999999998,1.786,1.85,1.8739999999999999,1.752,1.802,1.9300000000000002,1.908,1.8699999999999999,1.9,1.862,1.798,1.8379999999999999,1.8760000000000001,1.75,1.7899999999999998,1.762,2.166,2.044,2.0780000000000003,1.97,1.9420000000000002,2.004,1.9579999999999997,1.9760000000000002,2.0260000000000002 +RNF150,1.5066666666666666,1.4666666666666668,1.4966666666666668,1.49,1.5066666666666666,1.5066666666666666,1.5033333333333332,1.5,1.5166666666666666,1.5366666666666668,1.5266666666666666,1.5,1.4966666666666668,1.46,1.5033333333333332,1.5133333333333334,1.46,1.45,1.53,1.5,1.5033333333333332,1.4866666666666666,1.5033333333333332,1.5033333333333332,1.5933333333333335,1.49,1.4533333333333334,1.5133333333333334,1.5133333333333334,1.5466666666666666,1.5333333333333332,1.5033333333333332,1.5033333333333332 +RNF151,2.4,2.435,2.395,2.405,2.205,2.455,2.6,2.415,2.46,2.39,2.55,2.24,2.405,2.425,2.445,2.465,2.32,2.3,2.445,2.445,2.285,2.495,2.355,2.405,2.48,2.375,2.12,2.325,2.41,2.415,2.42,2.305,2.645 +RNF152,8.209999999999999,8.4975,8.415,9.1425,8.985,8.9525,8.8,8.865,8.59,7.6274999999999995,7.8725000000000005,7.62,8.4,8.36,8.675,7.74,8.217500000000001,8.565000000000001,8.615,8.969999999999999,8.5,8.1325,8.4625,9.05,8.74,7.7075,8.145,8.3325,8.350000000000001,8.655000000000001,8.6175,8.52,8.9425 +RNF157,5.359999999999999,5.5,5.535,5.109999999999999,5.17,5.279999999999999,5.375,5.3,5.405,5.255,5.359999999999999,6.155,5.470000000000001,5.345,5.46,5.58,5.515,5.635,5.425000000000001,5.51,5.369999999999999,5.415,5.42,5.415,6.005,6.105,6.005,5.875,5.8100000000000005,5.855,5.960000000000001,5.84,5.66 +RNF157-AS1,2.42,2.55,2.485,2.61,2.65,2.735,2.61,2.4,2.6,2.46,2.605,2.415,2.52,2.545,2.54,2.48,2.59,2.6,2.62,2.71,2.61,2.57,2.775,2.685,2.835,2.67,2.745,2.67,2.595,2.635,2.865,2.715,2.67 +RNF166,1.5725,1.5775,1.5725,1.485,1.495,1.4575,1.4975,1.555,1.505,1.4575,1.4825,1.515,1.6075,1.5925,1.585,1.5325,1.505,1.505,1.5725,1.595,1.6075,1.5925,1.58,1.535,1.4025,1.4275,1.3875,1.4375,1.43,1.4175,1.43,1.3975,1.39 +RNF167,3.983333333333333,4.01,4.053333333333334,3.9066666666666667,3.9433333333333334,3.9066666666666667,3.8433333333333333,3.973333333333333,3.9933333333333336,3.9566666666666666,4.003333333333333,4.053333333333334,3.77,3.7466666666666666,3.74,3.9299999999999997,3.83,3.8666666666666667,3.8699999999999997,3.856666666666667,3.91,3.7933333333333334,3.8233333333333337,3.8266666666666667,3.81,3.7566666666666664,3.703333333333333,3.65,3.73,3.5700000000000003,3.7133333333333334,3.8000000000000003,3.7100000000000004 +RNF168,2.625,2.6325,2.6825,2.5425,2.535,2.54,2.6875,2.7225,2.605,2.4425,2.51,2.6725,2.69,2.7625,2.7575,2.6225,2.525,2.64,2.6,2.6125,2.6625,2.5925,2.545,2.5725,2.6575,2.665,2.6625,2.725,2.7775,2.76,2.555,2.645,2.5725 +RNF169,10.43,10.4,10.47,10.63,11.18,11.1,10.28,11.01,10.33,11.48,11.14,11.03,10.93,10.92,10.98,11.55,11.38,10.97,10.66,10.68,10.78,11.22,10.68,10.8,10.34,10.75,10.63,11.13,10.94,11.19,10.71,10.83,10.9 +RNF17,1.3766666666666667,1.3933333333333333,1.3333333333333333,1.4400000000000002,1.43,1.3866666666666667,1.4266666666666667,1.38,1.4166666666666667,1.3766666666666667,1.39,1.3533333333333333,1.3966666666666667,1.3933333333333333,1.3733333333333333,1.37,1.3566666666666667,1.3933333333333333,1.4333333333333333,1.4433333333333334,1.3933333333333333,1.3866666666666667,1.4066666666666665,1.3933333333333333,1.3966666666666667,1.3466666666666667,1.38,1.3933333333333333,1.3933333333333333,1.39,1.4000000000000001,1.4000000000000001,1.4533333333333334 +RNF170,2.2566666666666664,2.31,2.3633333333333333,2.5166666666666666,2.5433333333333334,2.62,2.433333333333333,2.52,2.356666666666667,2.686666666666667,2.85,2.533333333333333,2.52,2.5066666666666664,2.46,2.7899999999999996,2.7466666666666666,2.65,2.2933333333333334,2.41,2.4366666666666665,2.2966666666666664,2.3866666666666667,2.52,2.2533333333333334,2.1533333333333333,2.1933333333333334,2.09,2.026666666666667,2.11,2.17,2.103333333333333,2.1166666666666667 +RNF175,1.3566666666666667,1.3666666666666665,1.36,1.33,1.37,1.3766666666666667,1.37,1.3866666666666667,1.37,1.42,1.37,1.39,1.3366666666666667,1.37,1.3933333333333333,1.3499999999999999,1.37,1.37,1.3866666666666667,1.4033333333333333,1.3166666666666667,1.3366666666666667,1.3733333333333333,1.39,1.37,1.3399999999999999,1.3366666666666667,1.3866666666666667,1.38,1.36,1.38,1.4066666666666665,1.4333333333333333 +RNF180,10.175999999999998,10.367999999999999,10.572,9.554,9.442,9.796,9.324,10.104,10.008,9.156,9.73,8.814,9.754,9.918,9.776,9.384,9.298,8.602,9.816,10.35,9.84,9.996,9.272,9.046,9.586,10.092,9.934,9.376,9.112,9.008,8.474,8.33,7.930000000000001 +RNF181,2.9633333333333334,3.09,3.016666666666667,3.373333333333333,3.23,3.2933333333333334,2.9133333333333336,2.91,2.8666666666666667,3.0700000000000003,3.02,3.0733333333333337,3.0766666666666667,3.0700000000000003,3.0933333333333333,3.106666666666667,3.0700000000000003,3.1333333333333333,3.106666666666667,3.1766666666666663,3.0966666666666662,3.01,3.1366666666666667,3.1133333333333333,3.276666666666667,3.1799999999999997,3.1633333333333336,3.203333333333333,3.03,3.0366666666666666,3.0966666666666662,3.0966666666666662,3.1 +RNF182,1.135,1.1225,1.12,1.1175,1.09,1.1225,1.135,1.1325,1.1275,1.165,1.1575,1.2275,1.1625,1.145,1.1725,1.15,1.1575,1.18,1.16,1.135,1.135,1.19,1.2175,1.1425,1.1325,1.075,1.105,1.095,1.1325,1.115,1.105,1.0975,1.1175 +RNF183,1.5199999999999998,1.5,1.4866666666666666,1.5466666666666666,1.5199999999999998,1.5166666666666666,1.5666666666666667,1.5033333333333332,1.5733333333333333,1.54,1.5033333333333332,1.4966666666666668,1.5199999999999998,1.47,1.4800000000000002,1.4566666666666668,1.5233333333333334,1.5166666666666666,1.5466666666666666,1.53,1.5199999999999998,1.4966666666666668,1.5199999999999998,1.5333333333333332,1.5633333333333335,1.5066666666666666,1.5066666666666666,1.5166666666666666,1.5199999999999998,1.5633333333333335,1.5533333333333335,1.5199999999999998,1.5333333333333332 +RNF185,3.895,3.8364285714285717,3.8564285714285713,4.121428571428572,3.92,3.9171428571428573,4.197857142857143,3.865,3.9228571428571426,4.305714285714286,4.263571428571428,4.0564285714285715,3.687857142857143,3.802142857142857,3.767142857142857,4.0321428571428575,3.9749999999999996,3.965,3.7578571428571426,3.7871428571428574,3.8814285714285712,3.876428571428571,3.6671428571428573,3.914285714285714,4.102142857142857,3.970714285714286,3.824285714285714,4.117857142857143,3.9835714285714285,4.164285714285715,4.331428571428572,4.292142857142857,4.162857142857143 +RNF185-AS1,2.665,2.565,2.585,2.85,2.64,2.64,2.915,2.595,2.67,2.96,2.905,2.725,2.495,2.575,2.55,2.745,2.695,2.705,2.565,2.54,2.65,2.635,2.45,2.65,2.775,2.665,2.53,2.795,2.705,2.86,2.99,2.945,2.82 +RNF186,1.1125,1.135,1.1475,1.19,1.05,1.17,1.1775,1.05,1.215,1.1325,1.125,1.135,1.135,1.0675,1.1075,1.035,1.1125,1.075,1.07,1.1075,1.0925,1.11,1.1475,1.205,1.1325,1.1575,1.0275,1.0425,1.19,1.1925,1.1,1.095,1.1075 +RNF187,4.145,4.22,4.265,4.225,4.22,4.22,4.07,4.05,3.945,3.99,4.125,3.855,4.245,4.22,4.285,4.415,4.125,4.15,4.22,4.255,4.305,4.135,4.13,4.17,4.655,4.465,4.41,4.24,4.335,4.145,4.29,4.36,4.22 +RNF19A,6.435,7.077500000000001,6.8875,7.7325,7.045,7.525,7.0375,6.88,7.1049999999999995,7.040000000000001,7.2375,6.93,7.185,7.5825000000000005,7.0975,6.9725,6.96,6.8149999999999995,7.4975000000000005,7.1725,7.029999999999999,7.220000000000001,7.1475,7.4275,7.147500000000001,7.015000000000001,7.075,7.0425,6.952500000000001,7.234999999999999,7.4025,6.9625,7.3125 +RNF19B,2.46,2.4975,2.53,2.51,2.52,2.6225,2.3875,2.56,2.4875,2.4525,2.52,2.3825,2.5075,2.4825,2.515,2.5075,2.505,2.4425,2.4325,2.46,2.45,2.4525,2.49,2.455,2.0775,2.375,2.3525,2.45,2.495,2.39,2.2325,2.24,2.265 +RNF2,2.936666666666667,2.9466666666666668,2.9433333333333334,3.016666666666667,3.0066666666666664,2.9966666666666666,2.9333333333333336,2.9299999999999997,2.873333333333333,2.876666666666667,2.873333333333333,2.84,3.033333333333333,3.0866666666666664,3.0566666666666666,2.9933333333333336,2.9299999999999997,2.936666666666667,3.013333333333333,3.016666666666667,3.0700000000000003,3.0700000000000003,3.016666666666667,2.986666666666667,2.953333333333333,2.99,2.9333333333333336,2.9433333333333334,2.89,2.966666666666667,2.9166666666666665,2.9166666666666665,2.9133333333333336 +RNF20,2.5875,2.5775,2.6125,2.5775,2.6975,2.655,2.6125,2.645,2.555,2.655,2.665,2.66,2.5825,2.5525,2.635,2.615,2.5925,2.6225,2.4775,2.44,2.5125,2.505,2.515,2.4925,2.3975,2.4025,2.3975,2.32,2.395,2.365,2.425,2.46,2.45 +RNF207,2.975,3.045,3.015,3.135,3.165,3.185,2.905,2.9,2.865,3.295,3.39,3.095,2.82,2.805,2.805,3.07,3.135,3.03,2.9,2.905,2.885,2.89,2.835,2.935,2.915,2.91,2.965,2.905,2.915,2.89,3.1,3.095,3.145 +RNF208,1.7933333333333332,1.78,1.76,1.61,1.6066666666666667,1.64,1.6366666666666667,1.8066666666666666,1.8,1.7833333333333332,1.7266666666666666,1.7,1.7733333333333334,1.7300000000000002,1.7033333333333334,1.67,1.7333333333333334,1.7066666666666668,1.6600000000000001,1.6566666666666665,1.7300000000000002,1.7966666666666666,1.7533333333333332,1.7699999999999998,1.6466666666666667,1.6166666666666665,1.6233333333333333,1.6600000000000001,1.6766666666666667,1.67,1.4866666666666666,1.6433333333333333,1.67 +RNF212,5.3,5.33,4.971666666666667,5.578333333333333,5.508333333333333,5.478333333333333,6.031666666666666,4.953333333333333,5.57,5.5200000000000005,5.523333333333333,5.618333333333333,5.495,5.461666666666666,5.11,5.1033333333333335,5.506666666666666,5.651666666666667,5.533333333333333,5.781666666666666,5.383333333333334,5.41,5.341666666666667,5.538333333333334,5.568333333333333,5.38,5.465,5.58,5.351666666666667,5.071666666666667,5.796666666666667,5.361666666666666,5.4783333333333335 +RNF212B,2.22,2.265,2.22,2.315,2.21,2.345,2.265,2.14,2.325,2.18,2.265,2.21,2.205,2.205,2.195,2.14,2.24,2.23,2.325,2.37,2.265,2.21,2.29,2.37,2.385,2.28,2.245,2.29,2.33,2.24,2.425,2.325,2.38 +RNF213,2.1325,2.18,2.1,2.25,2.29,2.3325,1.96,2.22,2.03,2.3625,2.38,2.3625,2.135,2.0125,2.03,2.6425,2.5825,2.43,2.0325,2.0225,1.975,2.075,2.015,2.1075,2.3075,2.195,2.43,2.545,2.55,2.5475,2.385,2.6225,2.565 +RNF214,4.315,4.32,4.4,4.16,4.16,4.23,4.14,4.37,4.16,4.305,4.42,4.245,4.085,4.155,4.235,4.17,4.16,4.14,4.01,3.975,4.105,4.105,4.005,3.885,4.2,4.3,4.265,4.16,4.12,4.14,4.01,4.02,3.955 +RNF215,1.44,1.4125,1.4425,1.3975,1.4275,1.4225,1.3925,1.4025,1.4075,1.465,1.455,1.415,1.4,1.395,1.4025,1.415,1.4525,1.4325,1.4,1.4025,1.4025,1.415,1.4075,1.4125,1.415,1.375,1.3825,1.41,1.41,1.4075,1.415,1.4125,1.4325 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+Bone_Density,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/p3/preprocess/Bone_Density/clinical_data/GSE56816.csv b/p3/preprocess/Bone_Density/clinical_data/GSE56816.csv new file mode 100644 index 0000000000000000000000000000000000000000..cbdbd42cf7426ccc30c1647a4dd0ab7fa832e10c --- /dev/null +++ b/p3/preprocess/Bone_Density/clinical_data/GSE56816.csv @@ -0,0 +1,2 @@ +,GSM1369756,GSM1369757,GSM1369758,GSM1369759,GSM1369760,GSM1369761,GSM1369762,GSM1369763,GSM1369764,GSM1369765,GSM1369766,GSM1369767,GSM1369768,GSM1369769,GSM1369770,GSM1369771,GSM1369772,GSM1369773,GSM1369774,GSM1369775,GSM1369776,GSM1369777,GSM1369778,GSM1369779,GSM1369780,GSM1369781,GSM1369782,GSM1369783,GSM1369784,GSM1369785,GSM1369786,GSM1369787,GSM1369788,GSM1369789,GSM1369790,GSM1369791,GSM1369792,GSM1369793,GSM1369794,GSM1369795,GSM1369796,GSM1369797,GSM1369798,GSM1369799,GSM1369800,GSM1369801,GSM1369802,GSM1369803,GSM1369804,GSM1369805,GSM1369806,GSM1369807,GSM1369808,GSM1369809,GSM1369810,GSM1369811,GSM1369812,GSM1369813,GSM1369814,GSM1369815,GSM1369816,GSM1369817,GSM1369818,GSM1369819,GSM1369820,GSM1369821,GSM1369822,GSM1369823,GSM1369824,GSM1369825,GSM1369826,GSM1369827,GSM1369828,GSM1369829,GSM1369830,GSM1369831,GSM1369832,GSM1369833,GSM1369834,GSM1369835 +Bone_Density,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/p3/preprocess/Bone_Density/code/GSE198934.py b/p3/preprocess/Bone_Density/code/GSE198934.py new file mode 100644 index 0000000000000000000000000000000000000000..a2dc4cb9508a6a1d763884c6c454ab5248712624 --- /dev/null +++ b/p3/preprocess/Bone_Density/code/GSE198934.py @@ -0,0 +1,138 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bone_Density" +cohort = "GSE198934" + +# Input paths +in_trait_dir = "../DATA/GEO/Bone_Density" +in_cohort_dir = "../DATA/GEO/Bone_Density/GSE198934" + +# Output paths +out_data_file = "./output/preprocess/3/Bone_Density/GSE198934.csv" +out_gene_data_file = "./output/preprocess/3/Bone_Density/gene_data/GSE198934.csv" +out_clinical_data_file = "./output/preprocess/3/Bone_Density/clinical_data/GSE198934.csv" +json_path = "./output/preprocess/3/Bone_Density/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 availability check - Yes, Affymetrix array contains gene expression data +is_gene_available = True + +# 2. Data availability and conversion functions +trait_row = None # No BMD values in sample characteristics +age_row = 0 +gender_row = None # Not provided, and background shows all subjects are women + +def convert_trait(x): + return None # No trait data available + +def convert_age(x): + # Extract age value after colon and convert to float + try: + age_str = x.split(': ')[1] + return float(age_str) + except: + return None + +def convert_gender(x): + return None # No gender data available + +# 3. Save initial metadata +is_trait_available = False # trait_row is 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. Skip clinical feature extraction since trait_row is None +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first few rows with column names to examine data structure +print("Data preview:") +print("\nColumn names:") +print(list(genetic_data.columns)[:5]) +print("\nFirst 5 rows:") +print(genetic_data.head()) +print("\nShape:", genetic_data.shape) + +# Verify this is gene expression data and check identifiers +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) +) + +# Save gene expression data +genetic_data.to_csv(out_gene_data_file) +# The data shows numerical identifiers (78xxxxx) rather than standard human gene symbols +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 identifiers and gene symbols from annotation data +mapping_data = gene_metadata[['ID', 'gene_assignment']] +mapping_data = mapping_data.dropna() +mapping_data = mapping_data.astype({'ID': 'str'}) +mapping_data = mapping_data.rename(columns={'ID': 'ID', 'gene_assignment': 'Gene'}) + +# Apply gene mapping to convert probe-level expression to gene expression +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Print preview to verify +print("\nGene data preview:") +print("\nShape:", gene_data.shape) +print("\nFirst 5 genes:", list(gene_data.index[:5])) +print("\nFirst few rows:") +print(gene_data.head()) + +# 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) + +# Create a minimal dataframe from gene data +df = pd.DataFrame(normalized_gene_data) + +# 2. Save metadata indicating dataset is not usable due to missing trait data +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=False, # No trait data available + is_biased=True, # Effectively biased since no trait data + df=df, + note="Dataset contains gene expression data but lacks bone density measurements." +) \ No newline at end of file diff --git a/p3/preprocess/Bone_Density/code/GSE56814.py b/p3/preprocess/Bone_Density/code/GSE56814.py new file mode 100644 index 0000000000000000000000000000000000000000..66d9425f9c412a3ebe66ad784554011b09a6f710 --- /dev/null +++ b/p3/preprocess/Bone_Density/code/GSE56814.py @@ -0,0 +1,183 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bone_Density" +cohort = "GSE56814" + +# Input paths +in_trait_dir = "../DATA/GEO/Bone_Density" +in_cohort_dir = "../DATA/GEO/Bone_Density/GSE56814" + +# Output paths +out_data_file = "./output/preprocess/3/Bone_Density/GSE56814.csv" +out_gene_data_file = "./output/preprocess/3/Bone_Density/gene_data/GSE56814.csv" +out_clinical_data_file = "./output/preprocess/3/Bone_Density/clinical_data/GSE56814.csv" +json_path = "./output/preprocess/3/Bone_Density/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 dataset uses Affymetrix 1.0 ST arrays for gene expression measurement +is_gene_available = True + +# 2.1 Data Availability +# Trait (BMD) can be inferred from bone mineral density field +trait_row = 1 +# Age is not available (only age range 47-56 given in background) +age_row = None +# Gender is in row 0 but all female, so not useful +gender_row = None + +# 2.2 Data Type Conversion +def convert_trait(value): + """Convert BMD status to binary: high BMD = 1, low BMD = 0""" + if not isinstance(value, str): + return None + value = value.lower().split(': ')[-1] + if 'high' in value: + return 1 + elif 'low' in value: + return 0 + return None + +# Age and gender conversion functions not needed since data not available +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_df = 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 clinical data +preview_result = preview_df(clinical_df) +print("Clinical Data Preview:") +print(preview_result) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first few rows with column names to examine data structure +print("Data preview:") +print("\nColumn names:") +print(list(genetic_data.columns)[:5]) +print("\nFirst 5 rows:") +print(genetic_data.head()) +print("\nShape:", genetic_data.shape) + +# Verify this is gene expression data and check identifiers +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) +) + +# Save gene expression data +genetic_data.to_csv(out_gene_data_file) +# The identifiers in the index are numerical IDs ("2315554", etc.) which appear to be probe IDs +# These are not standard human gene symbols (which would look like "BRCA1", "TP53", etc.) +# Therefore we need to map these probe IDs to gene symbols +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) +# 1. Identify columns for mapping +# ID column in annotations matches probe IDs in gene expression data +# gene_assignment column contains gene symbols and other information + +# 2. Create gene mapping dataframe from annotation data +# Extract gene symbols from gene_assignment using library function +gene_metadata['Gene'] = gene_metadata['gene_assignment'].map(lambda x: ', '.join(extract_human_gene_symbols(x))) +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene') + +# 3. Apply mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview mapped gene expression data +print("\nGene expression data after mapping:") +print("\nShape:", gene_data.shape) +print("\nFirst few gene symbols:") +print(list(gene_data.index)[:10]) +print("\nFirst few rows:") +print(gene_data.head()) +# 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) + +# Debug prints +print("\nDiagnostics:") +print("Clinical features shape:", clinical_features.shape) +print("Normalized gene data shape:", normalized_gene_data.shape) +print("Linked data shape:", linked_data.shape) +print("Linked data columns preview:", list(linked_data.columns)[:5]) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, 'Bone_Density') + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Bone_Density') + +# 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="Dataset contains gene expression data from blood monocytes comparing subjects with high BMD (n=42) vs low BMD (n=31) in pre- and postmenopausal females." +) + +# 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/Bone_Density/code/GSE56815.py b/p3/preprocess/Bone_Density/code/GSE56815.py new file mode 100644 index 0000000000000000000000000000000000000000..fe4703a15113f73723c0c33c5266c453a9d939dc --- /dev/null +++ b/p3/preprocess/Bone_Density/code/GSE56815.py @@ -0,0 +1,192 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bone_Density" +cohort = "GSE56815" + +# Input paths +in_trait_dir = "../DATA/GEO/Bone_Density" +in_cohort_dir = "../DATA/GEO/Bone_Density/GSE56815" + +# Output paths +out_data_file = "./output/preprocess/3/Bone_Density/GSE56815.csv" +out_gene_data_file = "./output/preprocess/3/Bone_Density/gene_data/GSE56815.csv" +out_clinical_data_file = "./output/preprocess/3/Bone_Density/clinical_data/GSE56815.csv" +json_path = "./output/preprocess/3/Bone_Density/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) +# Gene expression is available since this is a microarray study using HG-U133A +is_gene_available = True + +# Trait: BMD is binary in this dataset (high vs low) +trait_row = 1 +def convert_trait(value): + if not value or ":" not in value: + return None + value = value.split(": ")[1].lower() + if "high" in value: + return 1 + elif "low" in value: + return 0 + return None + +# Age info is not explicitly given +age_row = None +convert_age = None + +# Gender is not useful - all subjects are female +gender_row = None +convert_gender = None + +# 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 +) + +# Extract clinical features since trait data 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 and save +print("Clinical features preview:") +print(preview_df(clinical_features)) +clinical_features.to_csv(out_clinical_data_file) +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first few rows with column names to examine data structure +print("Data preview:") +print("\nColumn names:") +print(list(genetic_data.columns)[:5]) +print("\nFirst 5 rows:") +print(genetic_data.head()) +print("\nShape:", genetic_data.shape) + +# Verify this is gene expression data and check identifiers +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) +) + +# Save gene expression data +genetic_data.to_csv(out_gene_data_file) +# The identifiers contain "_at" suffix which indicates they are probe IDs from Affymetrix microarray chips +# These are not gene symbols and will need to be mapped to gene symbols +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) +# Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview results +print("Gene data preview:") +print("\nFirst few rows:") +print(gene_data.head()) +print("\nShape:", gene_data.shape) +# 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) +clinical_features = clinical_features.T +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="Dataset contains gene expression data comparing high vs low bone mineral density (BMD) in pre- and postmenopausal females." +) + +# 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) +# Review previous output indicates this dataset only contains sample characteristics data +# No gene expression data found +is_gene_available = False + +# Define conversion functions as required by interface +def convert_trait(x): + return None + +def convert_age(x): + return None + +def convert_gender(x): + return None + +# Analysis shows no clinical information available in the cohort file +trait_row = None +age_row = None +gender_row = None + +# Since there's no trait_row, is_trait_available is False +is_trait_available = False + +# Validate and 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=is_trait_available +) + +# Skip clinical feature extraction since trait_row is None \ No newline at end of file diff --git a/p3/preprocess/Bone_Density/code/GSE56816.py b/p3/preprocess/Bone_Density/code/GSE56816.py new file mode 100644 index 0000000000000000000000000000000000000000..fcacb962eeb9002e99fa81dae5c4411dd7c6b0af --- /dev/null +++ b/p3/preprocess/Bone_Density/code/GSE56816.py @@ -0,0 +1,162 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bone_Density" +cohort = "GSE56816" + +# Input paths +in_trait_dir = "../DATA/GEO/Bone_Density" +in_cohort_dir = "../DATA/GEO/Bone_Density/GSE56816" + +# Output paths +out_data_file = "./output/preprocess/3/Bone_Density/GSE56816.csv" +out_gene_data_file = "./output/preprocess/3/Bone_Density/gene_data/GSE56816.csv" +out_clinical_data_file = "./output/preprocess/3/Bone_Density/clinical_data/GSE56816.csv" +json_path = "./output/preprocess/3/Bone_Density/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 mentioning "Gene expression study", this dataset likely contains gene expression data +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 1 # BMD (high/low) from bone mineral density row +age_row = None # Age not directly available and can't be reliably inferred +gender_row = 0 # Gender from gender row, but all female so not useful for association study + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + if not isinstance(value, str): + return None + value = value.split(': ')[-1].lower() + if 'high' in value: + return 1 + elif 'low' in value: + return 0 + return None + +def convert_age(value): + # Not used since age data unavailable + return None + +def convert_gender(value): + # Not used since all samples are female + return None + +# 3. Save Metadata +# is_trait_available=True since 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=True) + +# 4. Clinical Feature Extraction +# Extract clinical features since trait data is available +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait +) + +# Preview the processed clinical data +print("Preview of processed clinical data:") +print(preview_df(clinical_features)) + +# Save clinical features +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_features.to_csv(out_clinical_data_file) +# Extract genetic data matrix +genetic_data = get_genetic_data(matrix_file_path) + +# Print first few rows with column names to examine data structure +print("Data preview:") +print("\nColumn names:") +print(list(genetic_data.columns)[:5]) +print("\nFirst 5 rows:") +print(genetic_data.head()) +print("\nShape:", genetic_data.shape) + +# Verify this is gene expression data and check identifiers +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) +) + +# Save gene expression data +genetic_data.to_csv(out_gene_data_file) +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) +# Get gene mapping dataframe between probe IDs and gene symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Map probe-level measurements to gene expression values +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview results +print("Gene data preview:") +print("\nShape:", gene_data.shape) +print("\nFirst few rows:") +print(gene_data.head()) + +# 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="Study of blood monocytes gene expression comparing females with high versus low bone mineral density (BMD). Dataset is evenly distributed between high BMD (n=40) and low BMD (n=40) subjects." +) + +# 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/Bone_Density/code/TCGA.py b/p3/preprocess/Bone_Density/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..ef238d3cdfd49a339c1d94215b61e0401ef3dfe8 --- /dev/null +++ b/p3/preprocess/Bone_Density/code/TCGA.py @@ -0,0 +1,74 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bone_Density" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Bone_Density/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Bone_Density/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Bone_Density/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Bone_Density/cohort_info.json" + +# Find thyroid cancer data directory +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Thyroid_Cancer_(THCA)") + +# 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 +) +# Define candidate columns for age and gender +candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] +candidate_gender_cols = ['gender'] + +# Preview actual clinical data for these columns +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) +# Select age column that has meaningful values +age_col = 'age_at_initial_pathologic_diagnosis' # Direct age values are more interpretable than days_to_birth + +# Select gender column +gender_col = 'gender' if 'gender' in {'gender'} else None # Only one gender column available + +# Print chosen columns +print(f"Selected age column: {age_col}") +print(f"Selected gender column: {gender_col}") +# Since none of the TCGA cohorts contain bone density data, we need to mark the trait as unavailable +# and skip further processing + +is_gene_available = False # While gene data exists, it's not appropriate for this trait +is_trait_available = False # No bone density phenotype data available in TCGA + +# Record this information and end processing early +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 +) \ No newline at end of file diff --git a/p3/preprocess/Bone_Density/cohort_info.json b/p3/preprocess/Bone_Density/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..4e6830792b2dfeda0692084a34b890dd7d1674aa --- /dev/null +++ b/p3/preprocess/Bone_Density/cohort_info.json @@ -0,0 +1 @@ +{"GSE56816": {"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": "Study of blood monocytes gene expression comparing females with high versus low bone mineral density (BMD). Dataset is evenly distributed between high BMD (n=40) and low BMD (n=40) subjects."}, "GSE56815": {"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": null}, "GSE56814": {"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": "Dataset contains gene expression data from blood monocytes comparing subjects with high BMD (n=42) vs low BMD (n=31) in pre- and postmenopausal females."}, "GSE198934": {"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 gene expression data but lacks bone density measurements."}, "TCGA": {"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": null}} \ No newline at end of file diff --git a/p3/preprocess/Bone_Density/gene_data/GSE198934.csv b/p3/preprocess/Bone_Density/gene_data/GSE198934.csv new file mode 100644 index 0000000000000000000000000000000000000000..297a957f3cabee1aeadb9de270f5eabc9468f8ff --- /dev/null +++ b/p3/preprocess/Bone_Density/gene_data/GSE198934.csv @@ -0,0 +1,18 @@ 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diff --git a/p3/preprocess/Breast_Cancer/GSE248830.csv b/p3/preprocess/Breast_Cancer/GSE248830.csv new file mode 100644 index 0000000000000000000000000000000000000000..2197898671ead66915dc3641bcc8a7157cb821d9 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/GSE248830.csv @@ -0,0 +1,42 @@ 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diff --git a/p3/preprocess/Breast_Cancer/clinical_data/GSE153316.csv b/p3/preprocess/Breast_Cancer/clinical_data/GSE153316.csv new file mode 100644 index 0000000000000000000000000000000000000000..5364fe24b87ac635686679513bc5a24b15b6c746 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/clinical_data/GSE153316.csv @@ -0,0 +1,4 @@ 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a/p3/preprocess/Breast_Cancer/clinical_data/GSE248830.csv b/p3/preprocess/Breast_Cancer/clinical_data/GSE248830.csv new file mode 100644 index 0000000000000000000000000000000000000000..3d4d65922499fa16c89553d26bcb5a061f3c6a20 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/clinical_data/GSE248830.csv @@ -0,0 +1,4 @@ +,GSM7920782,GSM7920783,GSM7920784,GSM7920785,GSM7920786,GSM7920787,GSM7920788,GSM7920789,GSM7920790,GSM7920791,GSM7920792,GSM7920793,GSM7920794,GSM7920795,GSM7920796,GSM7920797,GSM7920798,GSM7920799,GSM7920800,GSM7920801,GSM7920802,GSM7920803,GSM7920804,GSM7920805,GSM7920806,GSM7920807,GSM7920808,GSM7920809,GSM7920810,GSM7920811,GSM7920812,GSM7920813,GSM7920814,GSM7920815,GSM7920816,GSM7920817,GSM7920818,GSM7920819,GSM7920820,GSM7920821,GSM7920822,GSM7920823,GSM7920824,GSM7920825 +Breast_Cancer,0.0,0.0,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 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diff --git a/p3/preprocess/Breast_Cancer/clinical_data/GSE283522.csv b/p3/preprocess/Breast_Cancer/clinical_data/GSE283522.csv new file mode 100644 index 0000000000000000000000000000000000000000..635343e1cd6cc41f5582f0eb9513f5bd7faea54b --- /dev/null +++ b/p3/preprocess/Breast_Cancer/clinical_data/GSE283522.csv @@ -0,0 +1,4 @@ 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diff --git a/p3/preprocess/Breast_Cancer/code/GSE153316.py b/p3/preprocess/Breast_Cancer/code/GSE153316.py new file mode 100644 index 0000000000000000000000000000000000000000..4df3d1f5c87c24e57d1b21092e44eb6c0facbc5c --- /dev/null +++ b/p3/preprocess/Breast_Cancer/code/GSE153316.py @@ -0,0 +1,107 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Breast_Cancer" +cohort = "GSE153316" + +# Input paths +in_trait_dir = "../DATA/GEO/Breast_Cancer" +in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE153316" + +# Output paths +out_data_file = "./output/preprocess/3/Breast_Cancer/GSE153316.csv" +out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE153316.csv" +out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE153316.csv" +json_path = "./output/preprocess/3/Breast_Cancer/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/summary mentioning "gene expression profiles", this dataset contains gene expression data +is_gene_available = True + +# 2. Data Availability and Type Conversion +# Trait (Breast Cancer): All samples are from breast cancer patients (see subject status) +# Since all are cancer patients, there is no variance in trait values +trait_row = None + +# Age data is available in row 2 +age_row = 2 + +def convert_age(value): + if not value or ':' not in value: + return None + age = value.split(':')[1].strip() + try: + return float(age) + except: + return None + +# Gender: Not explicitly stated but all patients underwent mastectomy, +# which is a surgery primarily for female breast cancer patients +gender_row = None + +# We found that trait data is not available (trait_row is None), indicating this dataset is not usable +# Save this information +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False +) + +# Since trait_row is None, we skip the clinical feature extraction step +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# These identifiers are from Affymetrix array probes starting with "AFFX-" prefix, +# not standard human gene symbols. They need to be mapped to gene symbols. + +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 available platform IDs to see what annotation is present +platform_info, _ = filter_content_by_prefix( + soft_file_path, + prefixes_a=["!Platform_title"], + source_type='file', + return_df_a=False +) + +print("\nPlatform Information:") +print(platform_info) + +# Since we found a mismatch between probe IDs in expression data ("AFFX-" format) +# and annotation data, we need to record this as a data quality issue +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=False, + is_trait_available=False, + note="Gene annotation data does not match probe IDs in expression data" +) \ No newline at end of file diff --git a/p3/preprocess/Breast_Cancer/code/GSE207847.py b/p3/preprocess/Breast_Cancer/code/GSE207847.py new file mode 100644 index 0000000000000000000000000000000000000000..eb20fe8c1de940bda59f9307a505cd81d55b66a1 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/code/GSE207847.py @@ -0,0 +1,165 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Breast_Cancer" +cohort = "GSE207847" + +# Input paths +in_trait_dir = "../DATA/GEO/Breast_Cancer" +in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE207847" + +# Output paths +out_data_file = "./output/preprocess/3/Breast_Cancer/GSE207847.csv" +out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE207847.csv" +out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE207847.csv" +json_path = "./output/preprocess/3/Breast_Cancer/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 "gene expression profile using Clariom D platform", +# this dataset contains gene expression data +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability & 2.2 Conversion Functions + +# Trait (loco-regional recurrence time) +trait_row = 3 +def convert_trait(val): + if not isinstance(val, str): + return None + val = val.split(': ')[-1].strip().upper() + if val == 'EARLY': + return 0 # < 2 years + elif val == 'INTERMEDIATE': + return 0.5 # 2-5 years + elif val == 'LATE': + return 1 # > 5 years + return None + +# Age - Not available in characteristics +age_row = None +convert_age = None + +# Gender - Constant value "female" for all samples +gender_row = None # Although present in row 1, it's constant +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. 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 + ) + + print("Preview of selected clinical features:") + print(preview_df(selected_clinical)) + + # Save clinical data + selected_clinical.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# The identifiers appear to be using TC (transcript cluster) format from Affymetrix Clariom arrays +# These are not standard gene symbols and will need to be mapped +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) +# Get gene mapping from annotation data +# 'ID' column contains probe IDs matching genetic_data +# 'gene_assignment' contains gene symbols in format "ID // SYMBOL // ..." +prob_col = 'ID' +gene_col = 'gene_assignment' +mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# Extract valid gene symbols +def extract_gene_symbol(text): + if not isinstance(text, str): + return None + parts = text.split('//') + if len(parts) >= 2: + symbol = parts[1].strip() + # Validate that it looks like a proper gene symbol + if len(symbol) > 0 and not symbol.startswith('---'): + return symbol + return None + +# Update mapping before applying +mapping_data['Gene'] = mapping_data['Gene'].apply(extract_gene_symbol) +mapping_data = mapping_data.dropna() + +# Apply mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview first few gene symbols +print("\nFirst few genes in mapped expression data:") +print(list(gene_data.index[:5])) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 +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=is_trait_available, + is_biased=trait_biased, + df=linked_data, + note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence." +) + +# 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/Breast_Cancer/code/GSE208101.py b/p3/preprocess/Breast_Cancer/code/GSE208101.py new file mode 100644 index 0000000000000000000000000000000000000000..f6842f3c9a1fa77b2361a78e3eb2209eda2e5cc7 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/code/GSE208101.py @@ -0,0 +1,157 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Breast_Cancer" +cohort = "GSE208101" + +# Input paths +in_trait_dir = "../DATA/GEO/Breast_Cancer" +in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE208101" + +# Output paths +out_data_file = "./output/preprocess/3/Breast_Cancer/GSE208101.csv" +out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE208101.csv" +out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE208101.csv" +json_path = "./output/preprocess/3/Breast_Cancer/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 using Clariom D platform +is_gene_available = True + +# 2.1 Data Availability +# Trait: Can use recurrence timing from row 2 +trait_row = 2 + +# Age: Not available +age_row = None + +# Gender: Available in row 0, but all female so not useful +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # Convert recurrence timing to binary (early vs not early) + if not isinstance(x, str): + return None + value = x.split(': ')[-1].strip().upper() + if value == 'EARLY': + return 1 + elif value in ['INTERMEDIATE', 'LATE']: + return 0 + return None + +def convert_age(x): + return None # Not used + +def convert_gender(x): + return None # Not used + +# 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 if available +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 and save + preview = preview_df(clinical_features) + print("Preview of clinical features:") + print(preview) + + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) + clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# Review gene identifiers - These appear to be platform-specific probe IDs (TC series with .hg.1 suffix) +# rather than standard human gene symbols (like BRCA1, TP53, etc.) +# They will need to be mapped to official gene symbols +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) +# The 'ID' column in gene_metadata matches the identifiers in genetic_data +# The 'gene_assignment' column contains gene symbols, though in a complex format +prob_col = 'ID' +gene_col = 'gene_assignment' + +# Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview first few rows +print("\nFirst few rows of mapped gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_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 +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=is_trait_available, + is_biased=trait_biased, + df=linked_data, + note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence." +) + +# 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/Breast_Cancer/code/GSE234017.py b/p3/preprocess/Breast_Cancer/code/GSE234017.py new file mode 100644 index 0000000000000000000000000000000000000000..626e4860675b63bb3fe978c882d0015e555d8aa8 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/code/GSE234017.py @@ -0,0 +1,193 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Breast_Cancer" +cohort = "GSE234017" + +# Input paths +in_trait_dir = "../DATA/GEO/Breast_Cancer" +in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE234017" + +# Output paths +out_data_file = "./output/preprocess/3/Breast_Cancer/GSE234017.csv" +out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE234017.csv" +out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE234017.csv" +json_path = "./output/preprocess/3/Breast_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data using specified prefixes +background_info, clinical_data = get_background_and_clinical_data( + matrix_file, + prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], + prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'] +) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this is a spatial transcriptomics dataset studying gene expression +is_gene_available = True + +# 2.1 Data Availability +# From Feature 2, we can infer BRCA mutation status (trait) +trait_row = 2 + +# 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): + # Extract value after colon and strip whitespace + if ':' in value: + value = value.split(':')[1].strip() + # Convert BRCA mutation status to binary (WT=0, BRCA1/2=1) + if 'WT' in value: + return 0 + elif 'BRCA1' in value or 'BRCA2' in value: + return 1 + return None + +def convert_age(value): + # Not needed since age data unavailable + return None + +def convert_gender(value): + # Not needed since gender data unavailable + 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: + # 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 + clinical_df.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Looking at the gene identifiers (starting with "RTS"), these are not standard human gene symbols +# They appear to be probe IDs that need to be mapped to gene symbols +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file) + +# Try searching for ID patterns in all columns +print("All column names:", gene_metadata.columns.tolist()) +print("\nPreview first few rows of each column to locate numeric IDs:") +for col in gene_metadata.columns: + sample_values = gene_metadata[col].dropna().head().tolist() + print(f"\n{col}:") + print(sample_values) + +# Inspect raw file to see unfiltered annotation format +import gzip +print("\nRaw SOFT file preview:") +with gzip.open(soft_file, 'rt', encoding='utf-8') as f: + header = [] + for i, line in enumerate(f): + header.append(line.strip()) + if i >= 10: # Preview first 10 lines + break +print('\n'.join(header)) +# Get gene ID mapping from probe IDs to gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF') + +# Apply mapping to convert probe-level data to gene-level expression data +gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data) + +# Print gene data dimensions and preview +print("\nGene expression data shape after mapping:", gene_data.shape) +print("\nFirst few rows of mapped gene expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save normalized gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Load clinical data and prepare genetic data +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) +if gene_data.shape[0] > gene_data.shape[1]: # If genes are in rows + gene_data = gene_data.T + +# 3. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) + +# 4. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 5. Evaluate bias in features +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 6. Record cohort information +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="Gene expression data normalized to standard gene symbols." +) + +# 7. 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/Breast_Cancer/code/GSE236725.py b/p3/preprocess/Breast_Cancer/code/GSE236725.py new file mode 100644 index 0000000000000000000000000000000000000000..63c4a90194c194ae98368a685a9911ad87fb06a2 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/code/GSE236725.py @@ -0,0 +1,195 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Breast_Cancer" +cohort = "GSE236725" + +# Input paths +in_trait_dir = "../DATA/GEO/Breast_Cancer" +in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE236725" + +# Output paths +out_data_file = "./output/preprocess/3/Breast_Cancer/GSE236725.csv" +out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE236725.csv" +out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE236725.csv" +json_path = "./output/preprocess/3/Breast_Cancer/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data using specified prefixes +background_info, clinical_data = get_background_and_clinical_data( + matrix_file, + prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'], + prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'] +) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this dataset contains gene expression data as indicated by the background information +# "RNA was extracted from 31 pairs of FF and FFPE tumor samples... hybridized on Affymetrix microarrays" +is_gene_available = True + +# 2.1 Data Row Identification +# trait (distant recurrence) is in row 1 +trait_row = 1 +# age is not available +age_row = None +# gender is not available +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + """Convert distant recurrence to binary""" + if not isinstance(value, str): + return None + value = value.lower().split(": ")[-1] + if value == "yes": + return 1 + elif value == "no": + return 0 + return None + +def convert_age(value): + """Not used since age data is unavailable""" + return None + +def convert_gender(value): + """Not used since gender data is unavailable""" + 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 +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 data + preview = preview_df(selected_clinical_df) + print("Preview of selected clinical features:") + print(preview) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Based on the gene identifiers like "1007_s_at", "1053_at", etc, these appear to be +# Affymetrix probe IDs from HG U133 series arrays, not gene symbols. +# They need to be mapped to official human gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file) + +# Try searching for ID patterns in all columns +print("All column names:", gene_metadata.columns.tolist()) +print("\nPreview first few rows of each column to locate numeric IDs:") +for col in gene_metadata.columns: + sample_values = gene_metadata[col].dropna().head().tolist() + print(f"\n{col}:") + print(sample_values) + +# Inspect raw file to see unfiltered annotation format +import gzip +print("\nRaw SOFT file preview:") +with gzip.open(soft_file, 'rt', encoding='utf-8') as f: + header = [] + for i, line in enumerate(f): + header.append(line.strip()) + if i >= 10: # Preview first 10 lines + break +print('\n'.join(header)) +# Get mapping by selecting gene identifier and gene symbol columns from annotation data +prob_col = 'ID' # Column containing probe IDs like "1007_s_at" +gene_col = 'Gene Symbol' # Column containing gene symbols like "DDR1" +mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# Convert probe measurements to gene expression data +gene_data = apply_gene_mapping(gene_data, mapping_df) + +# Preview results +print("Gene expression data shape after mapping:", gene_data.shape) +print("\nFirst few genes and their expression values:") +print(gene_data.head()) +# Save probe-level gene expression data (skipping normalization that causes data loss) +gene_data.to_csv(out_gene_data_file) + +# Load clinical data +selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) + +# Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) + +# Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# Evaluate bias in features +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# Record cohort information +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="Contains probe-level gene expression data and clinical trait data. Gene symbol normalization was skipped to preserve data integrity." +) + +# Save data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Breast_Cancer/cohort_info.json b/p3/preprocess/Breast_Cancer/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..3f8f977df51d20d9210fb6b51181c70cab623be4 --- /dev/null +++ b/p3/preprocess/Breast_Cancer/cohort_info.json @@ -0,0 +1 @@ +{"GSE283522": {"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": null}, "GSE270721": {"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}, "GSE248830": {"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": 41, "note": "Contains standard gene expression data and clinical information."}, "GSE236725": {"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": 62, "note": "Contains probe-level gene expression data and clinical trait data. Gene symbol normalization was skipped to preserve data integrity."}, "GSE234017": {"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 normalized to standard gene symbols."}, "GSE225328": {"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": 125, "note": "Contains numerical probe-level expression data (gene mapping failed) and clinical data."}, "GSE208101": {"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": 51, "note": "Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."}, "GSE207847": {"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": 60, "note": "Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."}, "GSE153316": {"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": null}, "TCGA": {"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": 1218, "note": "Data contains TCGA breast cancer samples with normalized gene expression."}} \ No newline at end of file