Datasets:

Liu-Hy
/

GenoTEX

@@ -1853,3 +1853,6 @@ p3/preprocess/Kidney_Papillary_Cell_Carcinoma/gene_data/GSE68950.csv filter=lfs
 p3/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Kidney_stones/gene_data/GSE123993.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Kidney_stones/gene_data/GSE73680.csv filter=lfs diff=lfs merge=lfs -text

 p3/preprocess/Kidney_Clear_Cell_Carcinoma/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Kidney_stones/gene_data/GSE123993.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Kidney_stones/gene_data/GSE73680.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Lactose_Intolerance/gene_data/GSE136395.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Kidney_stones/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Lactose_Intolerance/gene_data/GSE138297.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Kidney_stones/gene_data/TCGA.csv ADDED Viewed

	@@ -0,0 +1,3 @@

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+size 97074045

p3/preprocess/LDL_Cholesterol_Levels/GSE181339.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

p3/preprocess/LDL_Cholesterol_Levels/clinical_data/GSE181339.csv ADDED Viewed

	@@ -0,0 +1,3 @@

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p3/preprocess/LDL_Cholesterol_Levels/clinical_data/GSE34945.csv ADDED Viewed

	@@ -0,0 +1,2 @@


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2	+ 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p3/preprocess/LDL_Cholesterol_Levels/code/GSE111567.py ADDED Viewed

	@@ -0,0 +1,120 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "LDL_Cholesterol_Levels"
+cohort = "GSE111567"
+# Input paths
+in_trait_dir = "../DATA/GEO/LDL_Cholesterol_Levels"
+in_cohort_dir = "../DATA/GEO/LDL_Cholesterol_Levels/GSE111567"
+# Output paths
+out_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/GSE111567.csv"
+out_gene_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/gene_data/GSE111567.csv"
+out_clinical_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/clinical_data/GSE111567.csv"
+json_path = "./output/preprocess/3/LDL_Cholesterol_Levels/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background info mentioning HumanHT-12 v4 microarray and gene expression analysis,
+# this dataset contains gene expression data
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# Trait (LDL cholesterol) is not directly available in clinical features
+trait_row = None
+# Age is not available in clinical features
+age_row = None
+# Gender is available at index 0
+gender_row = 0
+def convert_gender(x):
+    if x is None:
+        return None
+    value = x.split(': ')[1].strip()
+    if value.upper() == 'F':
+        return 0
+    elif value.upper() == 'M':
+        return 1
+    return None
+# Convert functions for completeness though trait and age not available
+def convert_trait(x):
+    return None
+def convert_age(x):
+    return None
+# 3. Save Metadata
+# Perform initial filtering and save cohort 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
+)
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None, indicating clinical data is not usable for our purpose
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# These are Illumina probe IDs, not standard human gene symbols
+# They need to be mapped to official HGNC gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+# Preview column names and first few values
+print("Gene Annotation Preview:")
+print(preview_df(gene_annotation))
+# 1. Observe gene identifiers:
+# Gene expression data uses 'ILMN_' probe IDs, which match the 'ID' column in annotation
+# Gene symbols are in the 'Symbol' column of annotation
+# 2. Extract mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')
+# 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)
+gene_data.to_csv(out_gene_data_file)
+# Create a minimal dataframe for validation purposes
+df_for_validation = pd.DataFrame(index=gene_data.index)
+df_for_validation[trait] = None  # Add trait column with all missing values
+note = "The dataset contains gene expression data from peripheral blood mononuclear cells measured with HumanHT-12 v4 microarray but lacks LDL cholesterol level measurements."
+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,
+    is_biased=True,  # Missing trait data is considered extreme bias
+    df=df_for_validation,
+    note=note
+)

p3/preprocess/LDL_Cholesterol_Levels/code/GSE181339.py ADDED Viewed

	@@ -0,0 +1,153 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "LDL_Cholesterol_Levels"
+cohort = "GSE181339"
+# Input paths
+in_trait_dir = "../DATA/GEO/LDL_Cholesterol_Levels"
+in_cohort_dir = "../DATA/GEO/LDL_Cholesterol_Levels/GSE181339"
+# Output paths
+out_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/GSE181339.csv"
+out_gene_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/gene_data/GSE181339.csv"
+out_clinical_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/clinical_data/GSE181339.csv"
+json_path = "./output/preprocess/3/LDL_Cholesterol_Levels/cohort_info.json"
+# Get paths for relevant files
+soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Extract background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_path)
+# Get unique values for each clinical feature
+sample_chars = get_unique_values_by_row(clinical_data)
+# Print dataset background information
+print("Background Information:")
+print(background_info)
+print("\nClinical Features Overview:")
+print(json.dumps(sample_chars, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - the background information mentions RNA extraction, microarray experiments
+is_gene_available = True
+# 2.1 Data Availability
+# LDL levels can be inferred from group (MONW has high LDL)
+trait_row = 1
+# Age data appears to be sample IDs rather than actual ages
+age_row = None
+# Gender data is available in row 0
+gender_row = 0
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    # Extract value after colon
+    x = x.split(': ')[-1].strip()
+    # MONW group has high LDL, other groups have normal LDL
+    if x == 'MONW':
+        return 1
+    elif x in ['NW', 'OW/OB']:
+        return 0
+    return None
+def convert_age(x):
+    # Not used since age data unreliable
+    return None
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    x = x.split(': ')[-1].strip()
+    if x.lower() == 'woman':
+        return 0
+    elif x.lower() == 'man':
+        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:
+    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 data
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    # Save clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data
+genetic_data = get_genetic_data(matrix_path)
+# Preview raw data structure
+print("First few rows of the raw data:")
+print(genetic_data.head())
+print("\nShape of the data:")
+print(genetic_data.shape)
+# Print first 20 row IDs to verify data structure
+print("\nFirst 20 probe/gene identifiers:")
+print(list(genetic_data.index)[:20])
+# From the pattern of gene identifiers being simple numbers like '7', '8', '15', etc.
+# These appear to be probe IDs rather than human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_path)
+# Preview annotation data structure
+print("Gene annotation data preview:")
+print(preview_df(gene_metadata))
+# Get mapping between gene 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 expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Preview gene data
+print("\nFirst few rows of gene expression data:")
+print(gene_data.head())
+print("\nShape of gene data:")
+print(gene_data.shape)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 4. Check for biased features and remove biased demographic ones
+# The function will print detailed distribution information
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Validate and save metadata about dataset quality
+# The validation is affected by if the trait is biased, if the data has been filtered out, etc.
+note = "This dataset compares gene expression between matched tumor and non-tumor kidney tissue samples."
+is_usable = validate_and_save_cohort_info(is_final=True, cohort=cohort, info_path=json_path,
+                                         is_gene_available=True, is_trait_available=True,
+                                         is_biased=trait_biased, df=linked_data, note=note)
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/LDL_Cholesterol_Levels/code/GSE28893.py ADDED Viewed

	@@ -0,0 +1,130 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "LDL_Cholesterol_Levels"
+cohort = "GSE28893"
+# Input paths
+in_trait_dir = "../DATA/GEO/LDL_Cholesterol_Levels"
+in_cohort_dir = "../DATA/GEO/LDL_Cholesterol_Levels/GSE28893"
+# Output paths
+out_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/GSE28893.csv"
+out_gene_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/gene_data/GSE28893.csv"
+out_clinical_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/clinical_data/GSE28893.csv"
+json_path = "./output/preprocess/3/LDL_Cholesterol_Levels/cohort_info.json"
+# Get paths for relevant files
+soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Extract background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_path)
+# Get unique values for each clinical feature
+sample_chars = get_unique_values_by_row(clinical_data)
+# Print dataset background information
+print("Background Information:")
+print(background_info)
+print("\nClinical Features Overview:")
+print(json.dumps(sample_chars, indent=2))
+# 1. Gene Expression Data Availability
+# The dataset is from Illumina Expression Array and is about gene expression in liver tissue
+is_gene_available = True
+# 2.1 Data Availability
+# From background info, this study includes eQTLs related to LDL cholesterol levels
+# But trait values are not directly available in sample characteristics
+trait_row = None
+# Age data is available in row 1
+age_row = 1
+# Gender data is available in row 2
+gender_row = 2
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Not needed since trait data is not available
+    return None
+def convert_age(x):
+    try:
+        # Extract number after colon
+        age = int(x.split(': ')[1])
+        return age
+    except:
+        return None
+def convert_gender(x):
+    try:
+        # Extract value after colon and convert to binary
+        gender = x.split(': ')[1]
+        if gender == 'F':
+            return 0
+        elif gender == 'M':
+            return 1
+        return None
+    except:
+        return None
+# 3. Save metadata - initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False
+)
+# 4. Skip clinical feature extraction since trait_row is None
+# Get gene expression data
+genetic_data = get_genetic_data(matrix_path)
+# Preview raw data structure
+print("First few rows of the raw data:")
+print(genetic_data.head())
+print("\nShape of the data:")
+print(genetic_data.shape)
+# Print first 20 row IDs to verify data structure
+print("\nFirst 20 probe/gene identifiers:")
+print(list(genetic_data.index)[:20])
+# These IDs start with "ILMN_" which indicates they are Illumina probe IDs, not gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data from SOFT file
+gene_metadata = get_gene_annotation(soft_path)
+# Preview annotation data structure
+print("Gene annotation data preview:")
+print(preview_df(gene_metadata))
+# 1. 'ID' column in metadata matches ILMN probe IDs in expression data
+# 'Symbol' column contains the gene symbols
+# 2. Get gene mapping data
+mapping_data = get_gene_mapping(gene_metadata, "ID", "Symbol")
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+# Preview result
+print("Gene expression data preview:")
+print(gene_data.head())
+print("\nShape after mapping:", gene_data.shape)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+# Since we previously determined trait data is not available (trait_row = None),
+# we cannot proceed with data linking and quality assessment
+# We need to validate this cohort as not usable
+note = "The dataset contains gene expression data but lacks LDL cholesterol level measurements"
+is_usable = validate_and_save_cohort_info(
+    is_final=False,  # Use initial filtering since we can't do final validation
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False
+)

p3/preprocess/LDL_Cholesterol_Levels/code/GSE34945.py ADDED Viewed

	@@ -0,0 +1,87 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "LDL_Cholesterol_Levels"
+cohort = "GSE34945"
+# Input paths
+in_trait_dir = "../DATA/GEO/LDL_Cholesterol_Levels"
+in_cohort_dir = "../DATA/GEO/LDL_Cholesterol_Levels/GSE34945"
+# Output paths
+out_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/GSE34945.csv"
+out_gene_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/gene_data/GSE34945.csv"
+out_clinical_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/clinical_data/GSE34945.csv"
+json_path = "./output/preprocess/3/LDL_Cholesterol_Levels/cohort_info.json"
+# Get paths for relevant files
+soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Extract background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_path)
+# Get unique values for each clinical feature
+sample_chars = get_unique_values_by_row(clinical_data)
+# Print dataset background information
+print("Background Information:")
+print(background_info)
+print("\nClinical Features Overview:")
+print(json.dumps(sample_chars, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background information, this study is about SNPs genotyping, not gene expression
+is_gene_available = False
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# LDL levels not directly given, check changed in apoc3 levels as proxy
+trait_row = 2
+# Age and gender not available in characteristics
+age_row = None
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract numeric value after colon
+    if isinstance(x, str) and "percent change in apoc3 levels:" in x:
+        try:
+            return float(x.split(":")[1].strip())
+        except:
+            return None
+    return None
+def convert_age(x):
+    return None  # Not available
+def convert_gender(x):
+    return None  # Not available
+# 3. Save Initial Metadata
+# Trait data is available since trait_row is not None
+is_trait_available = True if trait_row is not None else False
+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, 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)

p3/preprocess/LDL_Cholesterol_Levels/code/TCGA.py ADDED Viewed

	@@ -0,0 +1,179 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "LDL_Cholesterol_Levels"
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+# Output paths
+out_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/LDL_Cholesterol_Levels/cohort_info.json"
+# Cannot proceed with column identification without first having access to
+# the column names from the previous step's output
+# For now, define empty candidates
+candidate_age_cols = []
+candidate_gender_cols = []
+preview_dict = {}
+preview_dict
+# 1. From the subdirectories list, none contain terms directly related to LDL cholesterol or lipid levels
+# Therefore, we need to examine a proxy tissue/condition most related to cholesterol metabolism
+# The liver is the primary organ for cholesterol metabolism, so we'll use liver cancer data
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Liver_Cancer_(LIHC)')
+# 2. Get the clinical and genetic data 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())
+# Define candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'year_of_initial_pathologic_diagnosis']
+candidate_gender_cols = ['gender']
+# Use LIHC (Liver Cancer) data
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Liver_Cancer_(LIHC)")
+# Get clinical data path
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+clinical_df = pd.read_csv(clinical_file_path, index_col=0)
+# Preview age columns
+age_preview = {}
+for col in candidate_age_cols:
+    if col in clinical_df.columns:
+        age_preview[col] = clinical_df[col].head(5).tolist()
+print("Age columns preview:", age_preview)
+# Preview gender columns
+gender_preview = {}
+for col in candidate_gender_cols:
+    if col in clinical_df.columns:
+        gender_preview[col] = clinical_df[col].head(5).tolist()
+print("\nGender columns preview:", gender_preview)
+# Information from previous step
+# Dictionaries containing sample values from candidate columns
+age_candidates = {'age_at_initial_pathologic_diagnosis': [63, 53, 69, 65, 59], 'age_began_smoking_in_years': ['[Not Applicable]', '[Not Available]', '[Not Available]', '[Not Available]', '[Not Applicable]']}
+gender_candidates = {'gender': ['FEMALE', 'FEMALE', 'FEMALE', 'MALE', 'MALE']}
+# Select age column - choose 'age_at_initial_pathologic_diagnosis' as it has valid numeric values
+age_col = 'age_at_initial_pathologic_diagnosis' if 'age_at_initial_pathologic_diagnosis' in age_candidates and all(isinstance(x, (int, float)) for x in age_candidates['age_at_initial_pathologic_diagnosis']) else None
+# Select gender column - choose 'gender' if it contains valid gender values
+gender_col = 'gender' if 'gender' in gender_candidates and all(isinstance(x, str) and x.upper() in ['MALE', 'FEMALE'] for x in gender_candidates['gender']) else None
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# 1. Extract and standardize clinical features
+# First reload data with correct separator
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+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')
+# Use days_to_birth as a source for age calculation since LDL is a continuous trait
+age_values = (-clinical_df['days_to_birth']/365).round()
+age_values = age_values.fillna(age_values.mean()).astype(int)
+clinical_df['age_at_initial_pathologic_diagnosis'] = age_values
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col=age_col, gender_col=gender_col)
+# 2. Normalize gene symbols
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+# Save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+# 3. Link clinical and genetic data
+linked_data = pd.concat([selected_clinical_df, normalized_gene_df.T], axis=1)
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 5. Check for biased features and remove biased demographic features
+is_trait_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)
+# 6. Validate data quality and save cohort info
+note = "Data from TCGA Liver Cancer cohort used as proxy for LDL cholesterol studies due to liver's role in cholesterol metabolism. Age was calculated from days_to_birth for more accurate values."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA_LIHC",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=cleaned_data,
+    note=note
+)
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    cleaned_data.to_csv(out_data_file)
+    print(f"Data saved to {out_data_file}")
+else:
+    print("Data quality validation failed. Dataset not saved.")
+# 1. Extract and standardize clinical features
+# First reload data with correct separator
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+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')
+# Define demographic columns based on inspection from previous steps
+age_col = 'age_at_initial_pathologic_diagnosis'
+gender_col = 'gender'
+# Calculate age from days_to_birth for more accuracy
+age_values = (-clinical_df['days_to_birth']/365).round()
+age_values = age_values.fillna(age_values.mean()).astype(int)
+clinical_df[age_col] = age_values
+selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col=age_col, gender_col=gender_col)
+# 2. Normalize gene symbols
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+# Save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+# 3. Link clinical and genetic data
+linked_data = pd.concat([selected_clinical_df, normalized_gene_df.T], axis=1)
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 5. Check for biased features and remove biased demographic features
+is_trait_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)
+# 6. Validate data quality and save cohort info
+note = "Data from TCGA Liver Cancer cohort used as proxy for LDL cholesterol studies due to liver's role in cholesterol metabolism. Age was calculated from days_to_birth for more accurate values."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA_LIHC",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=cleaned_data,
+    note=note
+)
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    cleaned_data.to_csv(out_data_file)
+    print(f"Data saved to {out_data_file}")
+else:
+    print("Data quality validation failed. Dataset not saved.")

p3/preprocess/LDL_Cholesterol_Levels/cohort_info.json ADDED Viewed

	@@ -0,0 +1 @@

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p3/preprocess/LDL_Cholesterol_Levels/gene_data/GSE111567.csv ADDED Viewed

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p3/preprocess/LDL_Cholesterol_Levels/gene_data/GSE181339.csv ADDED Viewed

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p3/preprocess/Lactose_Intolerance/gene_data/GSE136395.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE114022.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE142494.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE145848.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE156309.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE159472.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE173263.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE197977.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE243973.csv ADDED Viewed

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p3/preprocess/Large_B-cell_Lymphoma/clinical_data/GSE248835.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM7920866,GSM7920867,GSM7920868,GSM7920869,GSM7920870,GSM7920871,GSM7920872,GSM7920873,GSM7920874,GSM7920875,GSM7920876,GSM7920877,GSM7920878,GSM7920879,GSM7920880,GSM7920881,GSM7920882,GSM7920883,GSM7920884,GSM7920885,GSM7920886,GSM7920887,GSM7920888,GSM7920889,GSM7920890,GSM7920891,GSM7920892,GSM7920893,GSM7920894,GSM7920895,GSM7920896,GSM7920897,GSM7920898,GSM7920899,GSM7920900,GSM7920901,GSM7920902,GSM7920903,GSM7920904,GSM7920905,GSM7920906,GSM7920907,GSM7920908,GSM7920909,GSM7920910,GSM7920911,GSM7920912,GSM7920913,GSM7920914,GSM7920915,GSM7920916,GSM7920917,GSM7920918,GSM7920919,GSM7920920,GSM7920921,GSM7920922,GSM7920923,GSM7920924,GSM7920925,GSM7920926,GSM7920927,GSM7920928,GSM7920929,GSM7920930,GSM7920931,GSM7920932,GSM7920933,GSM7920934,GSM7920935,GSM7920936,GSM7920937,GSM7920938,GSM7920939,GSM7920940,GSM7920941,GSM7920942,GSM7920943,GSM7920944,GSM7920945,GSM7920946,GSM7920947,GSM7920948,GSM7920949,GSM7920950,GSM7920951,GSM7920952,GSM7920953,GSM7920954,GSM7920955,GSM7920956,GSM7920957,GSM7920958,GSM7920959,GSM7920960,GSM7920961,GSM7920962,GSM7920963,GSM7920964,GSM7920965,GSM7920966,GSM7920967,GSM7920968,GSM7920969,GSM7920970,GSM7920971,GSM7920972,GSM7920973,GSM7920974,GSM7920975,GSM7920976,GSM7920977,GSM7920978,GSM7920979,GSM7920980,GSM7920981,GSM7920982,GSM7920983,GSM7920984,GSM7920985,GSM7920986,GSM7920987,GSM7920988,GSM7920989,GSM7920990,GSM7920991,GSM7920992,GSM7920993,GSM7920994,GSM7920995,GSM7920996,GSM7920997,GSM7920998,GSM7920999,GSM7921000,GSM7921001,GSM7921002,GSM7921003,GSM7921004,GSM7921005,GSM7921006,GSM7921007,GSM7921008,GSM7921009,GSM7921010,GSM7921011,GSM7921012,GSM7921013,GSM7921014,GSM7921015,GSM7921016,GSM7921017,GSM7921018,GSM7921019,GSM7921020,GSM7921021,GSM7921022,GSM7921023,GSM7921024,GSM7921025,GSM7921026,GSM7921027,GSM7921028,GSM7921029,GSM7921030,GSM7921031,GSM7921032,GSM7921033,GSM7921034,GSM7921035,GSM7921036,GSM7921037,GSM7921038,GSM7921039,GSM7921040,GSM7921041,GSM7921042,GSM7921043,GSM7921044,GSM7921045,GSM7921046,GSM7921047,GSM7921048,GSM7921049,GSM7921050,GSM7921051,GSM7921052,GSM7921053,GSM7921054,GSM7921055,GSM7921056,GSM7921057,GSM7921058,GSM7921059,GSM7921060,GSM7921061,GSM7921062,GSM7921063,GSM7921064,GSM7921065,GSM7921066,GSM7921067,GSM7921068,GSM7921069,GSM7921070,GSM7921071,GSM7921072,GSM7921073,GSM7921074,GSM7921075,GSM7921076,GSM7921077,GSM7921078,GSM7921079,GSM7921080,GSM7921081,GSM7921082,GSM7921083,GSM7921084,GSM7921085,GSM7921086,GSM7921087,GSM7921088,GSM7921089,GSM7921090,GSM7921091,GSM7921092,GSM7921093,GSM7921094,GSM7921095,GSM7921096,GSM7921097,GSM7921098,GSM7921099,GSM7921100,GSM7921101,GSM7921102,GSM7921103,GSM7921104,GSM7921105,GSM7921106,GSM7921107,GSM7921108,GSM7921109,GSM7921110,GSM7921111,GSM7921112,GSM7921113,GSM7921114,GSM7921115,GSM7921116,GSM7921117,GSM7921118,GSM7921119,GSM7921120,GSM7921121
2	+ Large_B-cell_Lymphoma,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Large_B-cell_Lymphoma/code/GSE114022.py ADDED Viewed

	@@ -0,0 +1,132 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE114022"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE114022"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE114022.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE114022.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE114022.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# The title and design indicate this is gene expression data from cell lines
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Treatment (YK-S vs YK-R) can be used as binary trait
+trait_row = 1
+# Cell lines only, no patient age data
+age_row = None
+# Cell lines only, no gender data
+gender_row = None
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(": ")[-1]
+    # YK-S vs YK-R comparison (exclude DMSO control)
+    if value == "yk-s":
+        return 0
+    elif value == "yk-r":
+        return 1
+    return None
+def convert_age(value):
+    # Not available
+    return None
+def convert_gender(value):
+    # Not available
+    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:
+    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 results
+    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)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# The gene identifiers start with "ILMN_" which indicates these are Illumina probe IDs
+# They need to be mapped to standard human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file)
+# Preview the annotation data structure
+print("Gene Annotation Preview:")
+preview = preview_df(gene_annotation)
+print(json.dumps(preview, indent=2))
+print("\nGene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE142494.py ADDED Viewed

	@@ -0,0 +1,118 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE142494"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE142494"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE142494.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE142494.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE142494.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the series description, this appears to be a gene expression study focused on B-cell differentiation
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data rows
+trait_row = 0  # Using cell type as trait indicator
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+# 2.2 Conversion functions
+def convert_trait(value: str) -> int:
+    """Convert cell type to binary: 1 for memory B cells, 0 for total B cells"""
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[-1].lower().strip()
+    if 'memory b cells' in value:
+        return 1
+    elif 'total b cells' in value:
+        return 0
+    return None
+def convert_age(value: str) -> float:
+    """Not used as age data is unavailable"""
+    return None
+def convert_gender(value: str) -> int:
+    """Not used as gender data is 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. 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 and save clinical data
+print("Clinical data preview:")
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# The identifiers start with "ILMN_", indicating they are Illumina probe IDs
+# These need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Report discovery of missing gene annotation
+print("Gene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE145848.py ADDED Viewed

	@@ -0,0 +1,115 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE145848"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE145848"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE145848.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE145848.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE145848.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Title mentions "transcription programs" and B cell cancers,
+# suggesting gene expression data will be part of the series
+is_gene_available = True
+# 2.1 Data Availability
+# From clinical features dictionary:
+# - trait (healthy vs CLL) is available in row 1
+# - age is not available
+# - gender is not available
+trait_row = 1
+age_row = None
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    # Convert to binary: 0 for healthy, 1 for disease
+    if 'healthy' in value:
+        return 0
+    elif 'chronic lymphocytic leukemia' in value:
+        return 1
+    return None
+convert_age = None
+convert_gender = None
+# 3. Save Metadata
+# Initial filtering - only checking data availability
+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 proceed with clinical feature extraction
+clinical_df = geo_select_clinical_features(clinical_data,
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait)
+# Preview the processed clinical data
+preview = preview_df(clinical_df)
+print("Clinical data preview:", preview)
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# The row indices appear to be probe identifiers from a microarray platform
+# (16657436, etc) rather than human gene symbols.
+# These need to be mapped to standard gene symbols for analysis.
+requires_gene_mapping = True
+# Report discovery of missing gene annotation
+print("Gene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE156309.py ADDED Viewed

	@@ -0,0 +1,123 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE156309"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE156309"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE156309.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE156309.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE156309.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Using Affymetrix Human U133 Plus 2.0 microarrays for mRNA expression
+# 2.1 Data Availability
+trait_row = 3  # 'disease status' indicates relapse status
+age_row = 0  # Age information is available
+gender_row = None  # No gender information available
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert relapse status to binary (0: relapse-free, 1: relapse)"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower()
+    if value == 'relapse-free':
+        return 0
+    elif value == 'relapse':
+        return 1
+    return None
+def convert_age(value: str) -> float:
+    """Convert age to continuous numeric value"""
+    if not isinstance(value, str):
+        return None
+    try:
+        age = float(value.split(': ')[-1])
+        return age
+    except:
+        return None
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (0: female, 1: male)"""
+    # Not used since gender data is 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:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the processed data
+    print(preview_df(selected_clinical_df))
+    # Save clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# These appear to be Affymetrix probe IDs (e.g. "1007_s_at", "AFFX-TrpnX-M_at")
+# rather than standard human gene symbols, so they will need to be mapped
+requires_gene_mapping = True
+# Report discovery of missing gene annotation
+print("Gene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE159472.py ADDED Viewed

	@@ -0,0 +1,169 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE159472"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE159472"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE159472.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE159472.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE159472.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Availability
+# Based on background info and series title, this is a microarray expression data for DLBCL
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row Numbers
+# Trait (ABC/GCB subtypes) is in row 2
+trait_row = 2
+# Age and gender not available in characteristics
+age_row = None
+gender_row = None
+# 2.2 Conversion Functions
+def convert_trait(x):
+    """Convert DLBCL subtype to binary: ABC=1, GCB=0"""
+    try:
+        if not isinstance(x, str):
+            return None
+        x = x.split(': ')[1].strip()
+        if 'ABC' in x:
+            return 1
+        elif 'GCB' in x:
+            return 0
+        return None
+    except:
+        return None
+def convert_age(x):
+    return None
+def convert_gender(x):
+    return None
+# 3. Save initial metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+# 4. Extract clinical features since trait data is available
+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 extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# Review gene identifiers - these appear to be Affymetrix probe IDs (e.g. "1007_s_at")
+# rather than standard human gene symbols, so mapping will be required
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file)
+# Print information about annotation data
+print("Gene Annotation Preview:")
+print("\nDataFrame Shape:", gene_annotation.shape)
+print("\nColumn Names:")
+print(gene_annotation.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_annotation))
+# Get mapping between gene IDs and gene symbols from annotation data
+# 'ID' column matches probe IDs in expression data, 'Gene Symbol' contains human gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+# Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Print info about the resulting gene expression data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few mapped genes and their expression values:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+# Debug print to check data before handling missing values
+print("\nPreview of linked data before handling missing values:")
+print(linked_data.head())
+# 3. Handle missing values
+linked_data = handle_missing_values(df=linked_data, trait_col=trait)
+print("\nPreview of linked data after handling missing values:")
+print(linked_data.head())
+# 4. Check for biases and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Validate dataset quality and save metadata
+note = ""
+if is_biased:
+    note = "The trait distribution is severely biased."
+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=note
+)
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE173263.py ADDED Viewed

	@@ -0,0 +1,120 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE173263"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE173263"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE173263.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE173263.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE173263.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info, this is a GEP (Gene Expression Profile) study
+is_gene_available = True
+# 2. Data Availability and Type Conversion
+# 2.1 Data Availability
+# Trait (response to R-CHOP) is in row 2
+trait_row = 2
+# Age not available
+age_row = None
+# Gender 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().split(": ")[-1].strip()
+    if "early failure" in value:
+        return 1
+    elif "remission" in value:
+        return 0
+    return None
+def convert_age(value):
+    return None
+def convert_gender(value):
+    return None
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview extracted features
+    preview = preview_df(clinical_features)
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# Based on the index format (e.g., '11715100_at', '11715101_s_at'), these appear to be Affymetrix probe IDs
+# rather than standard human gene symbols. They need to be mapped to HGNC gene symbols.
+requires_gene_mapping = True
+# Report discovery of missing gene annotation
+print("Gene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE182362.py ADDED Viewed

	@@ -0,0 +1,69 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE182362"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE182362"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE182362.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE182362.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE182362.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# This is a miRNA study on cell lines, not gene expression data
+is_gene_available = False
+# 2. Clinical Data Availability and Type Conversion
+# 2.1 Data rows
+# Only has treatment data in row 2, but no human trait/age/gender data
+trait_row = None
+age_row = None
+gender_row = None
+# 2.2 Conversion functions
+def convert_trait(x):
+    # Not used since trait data not available
+    return None
+def convert_age(x):
+    # Not used since age data not available
+    return None
+def convert_gender(x):
+    # Not used since gender data not available
+    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. Clinical Feature Extraction
+# Skip since trait_row is None, indicating no clinical data available

p3/preprocess/Large_B-cell_Lymphoma/code/GSE197977.py ADDED Viewed

	@@ -0,0 +1,122 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE197977"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE197977"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE197977.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE197977.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE197977.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the series title and summary, this dataset studies tumor gene expression and immune cell signatures,
+# so it should contain gene expression data
+is_gene_available = True
+# 2. Data Availability and Type Conversion
+# 2.1 Row identifiers
+trait_row = 2  # bestresponse contains response to treatment (CR/PR vs PD/SD)
+age_row = None  # Age data not available
+gender_row = None  # Gender data not available
+# 2.2 Type conversion functions
+def convert_trait(value: str) -> int:
+    """Convert treatment response to binary outcome
+    Complete Response (CR) and Partial Response (PR) -> 1 (response)
+    Stable Disease (SD) and Progressive Disease (PD) -> 0 (no response)
+    """
+    if not value or 'bestresponse:' not in value:
+        return None
+    response = value.split('bestresponse:')[1].strip()
+    if response in ['CR', 'PR']:
+        return 1
+    elif response in ['SD', 'PD']:
+        return 0
+    return None
+def convert_age(value: str) -> float:
+    return None
+def convert_gender(value: str) -> int:
+    return None
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted features
+    preview_dict = preview_df(clinical_features)
+    # 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)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# The row indices shown are simple numeric values (1, 2, 3 etc) which are not gene symbols
+# We need to map these numeric identifiers to proper gene symbols for biological interpretation
+requires_gene_mapping = True
+# Report discovery of missing gene annotation
+print("Gene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE243973.py ADDED Viewed

	@@ -0,0 +1,133 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE243973"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE243973"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE243973.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE243973.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE243973.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - Series summary mentions transcriptomic profiling
+is_gene_available = True
+# 2.1 Feature Key Identification
+# Trait - Row 0 contains disease state info
+trait_row = 0
+# Age - Not available in characteristics
+age_row = None
+# Gender - Not available in characteristics
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> int:
+    """Convert disease status to binary: 1 for LBCL, 0 for control"""
+    if pd.isna(x):
+        return None
+    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
+    if 'large b-cell lymphoma' in value:
+        return 1
+    elif 'healthy control' in value:
+        return 0
+    return None
+def convert_age(x: str) -> float:
+    """Not used but defined for completeness"""
+    return None
+def convert_gender(x: str) -> int:
+    """Not used but defined for completeness"""
+    return None
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# 4. Extract Clinical Features
+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 data
+    preview = preview_df(clinical_features)
+    print("Clinical features preview:", preview)
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# These appear to be standard human gene symbols (HGNC format)
+# e.g. ABCF1, ACACA, ADAR are well-known human gene symbols
+# No mapping needed as they are already in the correct format
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+genetic_data = normalize_gene_symbols_in_index(genetic_data)
+genetic_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(df=linked_data, trait_col=trait)
+# 4. Check for biases and remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Validate dataset quality and save metadata
+note = ""
+if is_biased:
+    note = "The trait distribution is severely biased."
+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=note
+)
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Large_B-cell_Lymphoma/code/GSE248835.py ADDED Viewed

	@@ -0,0 +1,109 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+cohort = "GSE248835"
+# Input paths
+in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
+in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE248835"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE248835.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE248835.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE248835.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on background info mentioning gene expression signatures
+# 2.1 Data Availability
+trait_row = 10  # histologically.proven.dlbcl.group indicates disease subtype
+age_row = None  # Age not available in characteristics
+gender_row = None  # Gender not available in characteristics
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[-1]
+    # Binary coding: DLBCL+Others as 0, HGBL as 1
+    if val == 'DLBCL+Others':
+        return 0
+    elif val == 'HGBL':
+        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 initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_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 of extracted clinical features:")
+    print(preview_df(selected_df))
+    # Save to CSV
+    selected_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# These appear to be numerical indices rather than proper gene symbols
+# Human gene symbols are typically alphanumeric strings like 'BRCA1', 'TP53', etc.
+# Therefore mapping will be required to convert these numeric IDs to gene symbols
+requires_gene_mapping = True
+# Report discovery of missing gene annotation
+print("Gene Annotation Analysis:")
+print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
+print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")
+# Update validation info to show dataset cannot be used due to missing gene mapping
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=False,  # Set to False since gene expression data is not mappable
+    is_trait_available=trait_row is not None,
+    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
+)

p3/preprocess/Large_B-cell_Lymphoma/code/TCGA.py ADDED Viewed

	@@ -0,0 +1,166 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Large_B-cell_Lymphoma"
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+# Output paths
+out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
+# 1. From the subdirectories list, select Large B-cell Lymphoma (DLBC) data since it matches our target trait
+cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Large_Bcell_Lymphoma_(DLBC)')
+# 2. Get the clinical and genetic data 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())
+# First check available directories
+import os
+print("Available directories:", os.listdir(tcga_root_dir))
+# Define candidate columns for age and gender
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+# Large B-cell Lymphoma corresponds to DLBC (Diffuse Large B-Cell Lymphoma) in TCGA nomenclature
+cohort_dir = [os.path.join(tcga_root_dir, d) for d in os.listdir(tcga_root_dir)
+              if "DLBC" in d][0]
+# Get clinical data file path
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+# Read clinical data
+clinical_df = pd.read_csv(clinical_file_path, index_col=0)
+# Extract and preview age columns
+age_preview = {}
+for col in candidate_age_cols:
+    age_preview[col] = clinical_df[col].head(5).tolist()
+print("Age columns preview:", age_preview)
+# Extract and preview gender columns
+gender_preview = {}
+for col in candidate_gender_cols:
+    gender_preview[col] = clinical_df[col].head(5).tolist()
+print("\nGender columns preview:", gender_preview)
+# Get the cohort directory path
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Large_Bcell_Lymphoma_(DLBC)")
+# Get clinical file path
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+# Read clinical data with tab separator
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+# Extract candidate demographic columns
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "_age_at_initial_pathologic_diagnosis"]
+candidate_gender_cols = ["gender"]
+# Preview candidate columns if they exist in the data
+demo_preview = {}
+if any(col in clinical_df.columns for col in candidate_age_cols):
+    for col in candidate_age_cols:
+        if col in clinical_df.columns:
+            demo_preview[col] = clinical_df[col].head().tolist()
+if any(col in clinical_df.columns for col in candidate_gender_cols):
+    for col in candidate_gender_cols:
+        if col in clinical_df.columns:
+            demo_preview[col] = clinical_df[col].head().tolist()
+print("candidate_age_cols =", candidate_age_cols)
+print("candidate_gender_cols =", candidate_gender_cols)
+print("\nPreview of demographic columns:")
+print(demo_preview)
+# Store the preview data
+preview_dict = {'age_at_initial_pathologic_diagnosis': [75, 67, 40, 73, 58], 'gender': ['MALE', 'MALE', 'MALE', 'MALE', 'FEMALE']}
+# Check age columns
+age_col = None
+if candidate_age_cols:
+    # Select first age column that has valid age values
+    for col in candidate_age_cols:
+        if col in preview_dict and any(isinstance(x, (int, float)) or (isinstance(x, str) and str(x).strip().isdigit()) for x in preview_dict[col]):
+            age_col = col
+            break
+# Check gender columns
+gender_col = None
+if candidate_gender_cols:
+    # Select first gender column that has valid gender values
+    for col in candidate_gender_cols:
+        if col in preview_dict and any(isinstance(x, str) and str(x).upper() in ['MALE', 'FEMALE'] for x in preview_dict[col]):
+            gender_col = col
+            break
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# 1. Extract and standardize clinical features
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+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')
+# Define demographic columns based on inspection from previous steps
+age_col = 'age_at_initial_pathologic_diagnosis'
+gender_col = 'gender'
+# Create a DataFrame with just the sample IDs to ensure proper trait encoding
+sample_ids = pd.DataFrame(index=genetic_df.columns)
+selected_clinical_df = tcga_select_clinical_features(sample_ids, trait, age_col=None, gender_col=None)
+# Add age and gender from clinical data if available
+if age_col in clinical_df.columns:
+    selected_clinical_df['Age'] = clinical_df[age_col]
+if gender_col in clinical_df.columns:
+    selected_clinical_df['Gender'] = clinical_df[gender_col].apply(tcga_convert_gender)
+# 2. Normalize gene symbols
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+# Save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+# 3. Link clinical and genetic data
+linked_data = pd.concat([selected_clinical_df, normalized_gene_df.T], axis=1)
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 5. Check for biased features and remove biased demographic features
+is_trait_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)
+# 6. Validate data quality and save cohort info
+note = "Data from TCGA Large B-cell Lymphoma (DLBC) cohort. Classification based on TCGA sample type codes (01-09: tumor, 10-19: normal)."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA_DLBC",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=cleaned_data,
+    note=note
+)
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    cleaned_data.to_csv(out_data_file)
+    print(f"Data saved to {out_data_file}")
+else:
+    print("Data quality validation failed. Dataset not saved.")

p3/preprocess/Large_B-cell_Lymphoma/cohort_info.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"GSE248835": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE243973": {"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": 85, "note": ""}, "GSE197977": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE182362": {"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}, "GSE173263": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE159472": {"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": 180, "note": ""}, "GSE156309": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE145848": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE142494": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE114022": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA_LIHC": {"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": 48, "note": "Data from TCGA Liver Cancer cohort used as proxy for LDL cholesterol studies due to liver's role in cholesterol metabolism. Age was calculated from days_to_birth for more accurate values."}, "TCGA_DLBC": {"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": 48, "note": "Data from TCGA Large B-cell Lymphoma (DLBC) cohort. Classification based on TCGA sample type codes (01-09: tumor, 10-19: normal)."}}

p3/preprocess/Large_B-cell_Lymphoma/gene_data/GSE243973.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

p3/preprocess/Liver_Cancer/clinical_data/GSE148346.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM4462080,GSM4462081,GSM4462082,GSM4462083,GSM4462084,GSM4462085,GSM4462086,GSM4462087,GSM4462088,GSM4462089,GSM4462090,GSM4462091,GSM4462092,GSM4462093,GSM4462094,GSM4462095,GSM4462096,GSM4462097,GSM4462098,GSM4462099,GSM4462100,GSM4462101,GSM4462102,GSM4462103,GSM4462104,GSM4462105,GSM4462106,GSM4462107,GSM4462108,GSM4462109,GSM4462110,GSM4462111,GSM4462112,GSM4462113,GSM4462114,GSM4462115,GSM4462116,GSM4462117,GSM4462118,GSM4462119,GSM4462120,GSM4462121,GSM4462122,GSM4462123,GSM4462124,GSM4462125,GSM4462126,GSM4462127,GSM4462128,GSM4462129,GSM4462130,GSM4462131,GSM4462132,GSM4462133,GSM4462134,GSM4462135,GSM4462136,GSM4462137,GSM4462138,GSM4462139,GSM4462140,GSM4462141,GSM4462142,GSM4462143,GSM4462144,GSM4462145,GSM4462146,GSM4462147,GSM4462148,GSM4462149,GSM4462150,GSM4462151,GSM4462152,GSM4462153,GSM4462154,GSM4462155,GSM4462156,GSM4462157,GSM4462158,GSM4462159,GSM4462160,GSM4462161,GSM4462162,GSM4462163,GSM4462164,GSM4462165,GSM4462166,GSM4462167,GSM4462168,GSM4462169,GSM4462170,GSM4462171,GSM4462172,GSM4462173,GSM4462174,GSM4462175,GSM4462176,GSM4462177,GSM4462178,GSM4462179,GSM4462180,GSM4462181,GSM4462182,GSM4462183,GSM4462184,GSM4462185,GSM4462186,GSM4462187,GSM4462188,GSM4462189,GSM4462190,GSM4462191,GSM4462192,GSM4462193,GSM4462194,GSM4462195,GSM4462196,GSM4462197,GSM4462198,GSM4462199,GSM4462200,GSM4462201,GSM4462202,GSM4462203,GSM4462204,GSM4462205,GSM4462206,GSM4462207,GSM4462208
2	+ Liver_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0

p3/preprocess/Liver_Cancer/clinical_data/GSE164760.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM5018268,GSM5018269,GSM5018270,GSM5018271,GSM5018272,GSM5018273,GSM5018274,GSM5018275,GSM5018276,GSM5018277,GSM5018278,GSM5018279,GSM5018280,GSM5018281,GSM5018282,GSM5018283,GSM5018284,GSM5018285,GSM5018286,GSM5018287,GSM5018288,GSM5018289,GSM5018290,GSM5018291,GSM5018292,GSM5018293,GSM5018294,GSM5018295,GSM5018296,GSM5018297,GSM5018298,GSM5018299,GSM5018300,GSM5018301,GSM5018302,GSM5018303,GSM5018304,GSM5018305,GSM5018306,GSM5018307,GSM5018308,GSM5018309,GSM5018310,GSM5018311,GSM5018312,GSM5018313,GSM5018314,GSM5018315,GSM5018316,GSM5018317,GSM5018318,GSM5018319,GSM5018320,GSM5018321,GSM5018322,GSM5018323,GSM5018324,GSM5018325,GSM5018326,GSM5018327,GSM5018328,GSM5018329,GSM5018330,GSM5018331,GSM5018332,GSM5018333,GSM5018334,GSM5018335,GSM5018336,GSM5018337,GSM5018338,GSM5018339,GSM5018340,GSM5018341,GSM5018342,GSM5018343,GSM5018344,GSM5018345,GSM5018346,GSM5018347,GSM5018348,GSM5018349,GSM5018350,GSM5018351,GSM5018352,GSM5018353,GSM5018354,GSM5018355,GSM5018356,GSM5018357,GSM5018358,GSM5018359,GSM5018360,GSM5018361,GSM5018362,GSM5018363,GSM5018364,GSM5018365,GSM5018366,GSM5018367,GSM5018368,GSM5018369,GSM5018370,GSM5018371,GSM5018372,GSM5018373,GSM5018374,GSM5018375,GSM5018376,GSM5018377,GSM5018378,GSM5018379,GSM5018380,GSM5018381,GSM5018382,GSM5018383,GSM5018384,GSM5018385,GSM5018386,GSM5018387,GSM5018388,GSM5018389,GSM5018390,GSM5018391,GSM5018392,GSM5018393,GSM5018394,GSM5018395,GSM5018396,GSM5018397,GSM5018398,GSM5018399,GSM5018400,GSM5018401,GSM5018402,GSM5018403,GSM5018404,GSM5018405,GSM5018406,GSM5018407,GSM5018408,GSM5018409,GSM5018410,GSM5018411,GSM5018412,GSM5018413,GSM5018414,GSM5018415,GSM5018416,GSM5018417,GSM5018418,GSM5018419,GSM5018420,GSM5018421,GSM5018422,GSM5018423,GSM5018424,GSM5018425,GSM5018426,GSM5018427,GSM5018428,GSM5018429,GSM5018430,GSM5018431,GSM5018432,GSM5018433,GSM5018434,GSM5018435,GSM5018436,GSM5018437
2	+ Liver_Cancer,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Liver_Cancer/clinical_data/GSE174570.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM5319834,GSM5319835,GSM5319836,GSM5319837,GSM5319838,GSM5319839,GSM5319840,GSM5319841,GSM5319842,GSM5319843,GSM5319844,GSM5319845,GSM5319846,GSM5319847,GSM5319848,GSM5319849,GSM5319850,GSM5319851,GSM5319852,GSM5319853,GSM5319854,GSM5319855,GSM5319856,GSM5319857,GSM5319858,GSM5319859,GSM5319860,GSM5319861,GSM5319862,GSM5319863,GSM5319864,GSM5319865,GSM5319866,GSM5319867,GSM5319868,GSM5319869,GSM5319870,GSM5319871,GSM5319872,GSM5319873,GSM5319874,GSM5319875,GSM5319876,GSM5319877,GSM5319878,GSM5319879,GSM5319880,GSM5319881,GSM5319882,GSM5319883,GSM5319884,GSM5319885,GSM5319886,GSM5319887,GSM5319888,GSM5319889,GSM5319890,GSM5319891,GSM5319892,GSM5319893,GSM5319894,GSM5319895,GSM5319896,GSM5319897,GSM5319898,GSM5319899,GSM5319900,GSM5319901,GSM5319902,GSM5319903,GSM5319904,GSM5319905,GSM5319906,GSM5319907,GSM5319908,GSM5319909,GSM5319910,GSM5319911,GSM5319912,GSM5319913,GSM5319914,GSM5319915,GSM5319916,GSM5319917,GSM5319918,GSM5319919,GSM5319920,GSM5319921,GSM5319922,GSM5319923,GSM5319924,GSM5319925,GSM5319926,GSM5319927,GSM5319928,GSM5319929,GSM5319930,GSM5319931,GSM5319932,GSM5319933,GSM5319934,GSM5319935,GSM5319936,GSM5319937,GSM5319938,GSM5319939,GSM5319940,GSM5319941,GSM5319942,GSM5319943,GSM5319944,GSM5319945,GSM5319946,GSM5319947
2	+ Liver_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

p3/preprocess/Liver_Cancer/clinical_data/GSE228782.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM7136390,GSM7136391,GSM7136392,GSM7136393,GSM7136394,GSM7136395,GSM7136396,GSM7136397,GSM7136398,GSM7136399,GSM7136400,GSM7136401,GSM7136402,GSM7136403,GSM7136404,GSM7136405,GSM7136406,GSM7136407,GSM7136408,GSM7136409,GSM7136410,GSM7136411,GSM7136412,GSM7136413,GSM7136414,GSM7136415,GSM7136416,GSM7136417,GSM7136418,GSM7136419,GSM7136420,GSM7136421,GSM7136422,GSM7136423,GSM7136424,GSM7136425,GSM7136426,GSM7136427,GSM7136428,GSM7136429,GSM7136430,GSM7136431,GSM7136432,GSM7136433,GSM7136434,GSM7136435,GSM7136436,GSM7136437,GSM7136438,GSM7136439,GSM7136440,GSM7136441,GSM7136442,GSM7136443,GSM7136444,GSM7136445,GSM7136446,GSM7136447,GSM7136448,GSM7136449,GSM7136450,GSM7136451,GSM7136452,GSM7136453,GSM7136454,GSM7136455,GSM7136456,GSM7136457,GSM7136458,GSM7136460,GSM7136462,GSM7136465,GSM7136468,GSM7136471,GSM7136472,GSM7136473,GSM7136474,GSM7136475,GSM7136476,GSM7136477,GSM7136478,GSM7136479,GSM7136480
2	+ Liver_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

p3/preprocess/Liver_Cancer/clinical_data/GSE228783.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM7136321,GSM7136322,GSM7136323,GSM7136324,GSM7136325,GSM7136326,GSM7136327,GSM7136328,GSM7136329,GSM7136330,GSM7136331,GSM7136332,GSM7136333,GSM7136334,GSM7136335,GSM7136336,GSM7136337,GSM7136338,GSM7136339,GSM7136340,GSM7136341,GSM7136342,GSM7136343,GSM7136344,GSM7136345,GSM7136346,GSM7136347,GSM7136348,GSM7136349,GSM7136350,GSM7136351,GSM7136352,GSM7136353,GSM7136354,GSM7136355,GSM7136356,GSM7136357,GSM7136358,GSM7136359,GSM7136360,GSM7136361,GSM7136362,GSM7136363,GSM7136364,GSM7136365,GSM7136366,GSM7136367,GSM7136368,GSM7136369,GSM7136370,GSM7136371,GSM7136372,GSM7136373,GSM7136374,GSM7136375,GSM7136376,GSM7136377,GSM7136378,GSM7136379,GSM7136380,GSM7136381,GSM7136382,GSM7136383,GSM7136384,GSM7136385,GSM7136386,GSM7136387,GSM7136388,GSM7136389,GSM7136390,GSM7136391,GSM7136392,GSM7136393,GSM7136394,GSM7136395,GSM7136396,GSM7136397,GSM7136398,GSM7136399,GSM7136400,GSM7136401,GSM7136402,GSM7136403,GSM7136404,GSM7136405,GSM7136406,GSM7136407,GSM7136408,GSM7136409,GSM7136410,GSM7136411,GSM7136412,GSM7136413,GSM7136414,GSM7136415,GSM7136416,GSM7136417,GSM7136418,GSM7136419,GSM7136420,GSM7136421,GSM7136422,GSM7136423,GSM7136424,GSM7136425,GSM7136426,GSM7136427,GSM7136428,GSM7136429,GSM7136430,GSM7136431,GSM7136432,GSM7136433,GSM7136434,GSM7136435,GSM7136436,GSM7136437,GSM7136438,GSM7136439,GSM7136440,GSM7136441,GSM7136442,GSM7136443,GSM7136444,GSM7136445,GSM7136446,GSM7136447,GSM7136448,GSM7136449,GSM7136450,GSM7136451,GSM7136452,GSM7136453,GSM7136454,GSM7136455,GSM7136456,GSM7136457,GSM7136458,GSM7136460,GSM7136462,GSM7136465,GSM7136468,GSM7136471,GSM7136472,GSM7136473,GSM7136474,GSM7136475,GSM7136476,GSM7136477,GSM7136478,GSM7136479,GSM7136480
2	+ Liver_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0

p3/preprocess/Liver_Cancer/clinical_data/GSE45032.csv ADDED Viewed

	@@ -0,0 +1,4 @@

+,GSM1096016,GSM1096017,GSM1096018,GSM1096019,GSM1096020,GSM1096021,GSM1096022,GSM1096023,GSM1096024,GSM1096025,GSM1096026,GSM1096027,GSM1096028,GSM1096029,GSM1096030,GSM1096031,GSM1096032,GSM1096033,GSM1096034,GSM1096035,GSM1096036,GSM1096037,GSM1096038,GSM1096039,GSM1096040,GSM1096041,GSM1096042,GSM1096043,GSM1096044,GSM1096045,GSM1096046,GSM1096047,GSM1096048,GSM1096049,GSM1096050,GSM1096051,GSM1096052,GSM1096053,GSM1096054,GSM1096055,GSM1096056,GSM1096057,GSM1096058,GSM1096059,GSM1096060,GSM1096061,GSM1096062,GSM1096063
+Liver_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,67.0,56.0,76.0,79.0,66.0,70.0,68.0,72.0,62.0,66.0,55.0,62.0,71.0,73.0,74.0,61.0,54.0,64.0,68.0,59.0,79.0,69.0,59.0,71.0,64.0,55.0,66.0,56.0,66.0,68.0,25.0,41.0,50.0,56.0,66.0,58.0,67.0,49.0,63.0,70.0,60.0,50.0,58.0,61.0,60.0,59.0,52.0,51.0
+Gender,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0

p3/preprocess/Liver_Cancer/clinical_data/GSE66843.csv ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ ,GSM1633236,GSM1633237,GSM1633238,GSM1633239,GSM1633240,GSM1633241,GSM1633242,GSM1633243,GSM1633244,GSM1633245,GSM1633246,GSM1633247,GSM1633248,GSM1633249,GSM1633250,GSM1633251,GSM1633252
2	+ Liver_Cancer,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0

p3/preprocess/Liver_Cancer/clinical_data/TCGA.csv ADDED Viewed

	@@ -0,0 +1,439 @@

+sampleID,Liver_Cancer,Age,Gender
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p3/preprocess/Liver_Cancer/code/GSE148346.py ADDED Viewed

	@@ -0,0 +1,139 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Liver_Cancer"
+cohort = "GSE148346"
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_Cancer"
+in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE148346"
+# Output paths
+out_data_file = "./output/preprocess/3/Liver_Cancer/GSE148346.csv"
+out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE148346.csv"
+out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE148346.csv"
+json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"
+# Step 1: Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Step 2: Extract background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Step 3: Get dictionary of unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Step 4: Print background info and sample characteristics
+print("Dataset Background Information:")
+print("-" * 80)
+print(background_info)
+print("\nSample Characteristics:")
+print("-" * 80)
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background information, this appears to be a biopsy study with gene expression analysis
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# For trait, we can use tissue disease state (key 3) which indicates lesional (LS) vs non-lesional (NL) liver tissue
+trait_row = 3
+def convert_trait(x: str) -> Optional[int]:
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1]
+    if value == 'LS':
+        return 1  # Lesional
+    elif value == 'NL':
+        return 0  # Non-lesional
+    return None
+# No age information available
+age_row = None
+convert_age = None
+# No gender information available
+gender_row = None
+convert_gender = None
+# 3. Save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+# 4. Clinical feature extraction
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the data
+    print(preview_df(selected_clinical))
+    # Save to file
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file)
+# 1. Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# 2. Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(genetic_data.index[:20])
+# Those are Affymetrix probe IDs (_at suffix is characteristic of Affy arrays)
+# They need to be mapped to gene symbols for consistency and interpretability
+requires_gene_mapping = True
+# 1. Extract gene annotation data from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+# 2. Preview annotation data
+print("Column names and first few values in gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Get gene mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+# 2. Apply mapping to convert probe level data to gene level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Preview gene data
+print("\nFirst 5 genes and 5 samples 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)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 4. Judge if features are biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Save 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=trait_biased,
+    df=linked_data,
+    note="Expression array data of NASH-HCC patients and NASH controls. No age/gender information available."
+)
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Liver_Cancer/code/GSE164760.py ADDED Viewed

	@@ -0,0 +1,146 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Liver_Cancer"
+cohort = "GSE164760"
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_Cancer"
+in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE164760"
+# Output paths
+out_data_file = "./output/preprocess/3/Liver_Cancer/GSE164760.csv"
+out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE164760.csv"
+out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE164760.csv"
+json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"
+# Step 1: Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+# Step 2: Extract background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+# Step 3: Get dictionary of unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+# Step 4: Print background info and sample characteristics
+print("Dataset Background Information:")
+print("-" * 80)
+print(background_info)
+print("\nSample Characteristics:")
+print("-" * 80)
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# According to the series title and summary, this dataset focuses on molecular characterization with expression arrays
+is_gene_available = True
+# 2.1 Data Availability
+# - Trait (NASH-HCC vs non-tumoral) can be inferred from tissue type at row 0
+trait_row = 0
+# - Age is not available in sample characteristics
+age_row = None
+# - Gender is not available in sample characteristics
+gender_row = None
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert tissue type to binary trait value.
+    1: NASH-HCC tumor (case)
+    0: NASH liver, non-tumoral NASH liver (control)
+    None: Healthy liver, cirrhotic liver (excluded)
+    """
+    if not value or ':' not in value:
+        return None
+    tissue = value.split(':', 1)[1].strip().lower()
+    if 'nash-hcc tumor' in tissue:
+        return 1
+    elif 'nash liver' in tissue:
+        return 0
+    else:
+        return None
+def convert_age(value: str) -> Optional[float]:
+    return None  # Not used
+def convert_gender(value: str) -> Optional[int]:
+    return None  # Not used
+# 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_data_processed = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    # Preview the processed data
+    print("Preview of processed clinical data:")
+    print(preview_df(clinical_data_processed))
+    # Save to CSV
+    clinical_data_processed.to_csv(out_clinical_data_file)
+# 1. Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+# 2. Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(genetic_data.index[:20])
+# The identifiers in format '11715100_at' appear to be Affymetrix probeset IDs
+# rather than standard human gene symbols. They will need to be mapped to gene symbols.
+requires_gene_mapping = True
+# 1. Extract gene annotation data from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+# 2. Preview annotation data
+print("Column names and first few values in gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Identify columns for mapping
+# In the annotation data, 'ID' contains the same probe IDs as in gene_expression data
+# 'Gene Symbol' contains the corresponding gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+# 2. Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
+# 3. Apply mapping to convert probe-level data to gene-level 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)
+gene_data.to_csv(out_gene_data_file)
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_data_processed, gene_data)
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 4. Judge if features are biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Save 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=trait_biased,
+    df=linked_data,
+    note="Expression array data of NASH-HCC patients and NASH controls. No age/gender information available."
+)
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Liver_Cancer/code/GSE174570.py ADDED Viewed

	@@ -0,0 +1,81 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Liver_Cancer"
+cohort = "GSE174570"
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_Cancer"
+in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE174570"
+# Output paths
+out_data_file = "./output/preprocess/3/Liver_Cancer/GSE174570.csv"
+out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE174570.csv"
+out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE174570.csv"
+json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Yes - using Affymetrix Human Genome U219 Array
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# Disease state (trait) is in row 0, has two values (HCC vs control)
+trait_row = 0
+# Age and gender not available in characteristics
+age_row = None
+gender_row = None
+# Convert disease state to binary (HCC = 1, Non-tumour/control = 0)
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1]
+    if 'hcc' in value:
+        return 1
+    return 0
+def convert_age(value):
+    return None
+def convert_gender(value):
+    return None
+# 3. Save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(clinical_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))
+    selected_clinical.to_csv(out_clinical_data_file)

p3/preprocess/Liver_Cancer/code/GSE178201.py ADDED Viewed

	@@ -0,0 +1,136 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Liver_Cancer"
+cohort = "GSE178201"
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_Cancer"
+in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE178201"
+# Output paths
+out_data_file = "./output/preprocess/3/Liver_Cancer/GSE178201.csv"
+out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE178201.csv"
+out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE178201.csv"
+json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"
+# Get file paths for soft and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+# Get unique values for each clinical feature row
+clinical_features = get_unique_values_by_row(clinical_data)
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nClinical Features and Sample Values:")
+print(json.dumps(clinical_features, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info, this dataset does contain gene expression data (L1000 platform)
+is_gene_available = True
+# 2.1 Data Availability
+# Looking at sample characteristics, there is no trait (cancer status), age or gender info
+# These are cell line experiments, not patient samples
+trait_row = None
+age_row = None
+gender_row = None
+# 2.2 Data Type Conversion Functions
+# Not needed since we have no clinical data, but defining empty functions to satisfy interface
+def convert_trait(x):
+    return None
+def convert_age(x):
+    return None
+def convert_gender(x):
+    return None
+# 3. Save Metadata
+# Initial filtering - trait data not available (cell lines)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False
+)
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None (no clinical data available)
+# Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file)
+# Print DataFrame info and dimensions to verify data structure
+print("DataFrame info:")
+print(genetic_data.info())
+print("\nDataFrame dimensions:", genetic_data.shape)
+# Print an excerpt of the data to inspect row/column structure
+print("\nFirst few rows and columns of data:")
+print(genetic_data.head().iloc[:, :5])
+# Print first 20 row IDs
+print("\nFirst 20 gene/probe IDs:")
+print(genetic_data.index[:20].tolist())
+# The row index values appear to be Entrez Gene IDs
+# These are numerical identifiers that need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file)
+# Preview the annotation data structure
+print("Gene Annotation Preview:")
+print("\nColumns:", gene_annotation.columns.tolist())
+preview = preview_df(gene_annotation)
+print(json.dumps(preview, indent=2))
+# Get mapping between probe IDs and gene symbols
+prob_col = 'ID'  # Column containing probe IDs
+gene_col = 'pr_gene_symbol'  # Column containing gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+# Preview the mapping
+print("\nGene Mapping Preview:")
+mapping_preview = preview_df(mapping_df)
+print(json.dumps(mapping_preview, indent=2))
+# Apply gene mapping to convert probe IDs to gene symbols
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+# Print DataFrame info and preview to verify mapping result
+print("Gene Expression Data After Mapping:")
+print("\nDataFrame info:")
+print(gene_data.info())
+print("\nDataFrame dimensions:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20].tolist())
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+# Create an empty DataFrame for mock validation
+mock_df = pd.DataFrame({
+    trait: [0,1],  # Mock trait values
+    'GENE1': [0,0]  # Mock gene values
+})
+# Mark dataset as not usable in final validation due to lack of trait data
+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=True,  # Consider lack of trait data as biased
+    df=mock_df,
+    note="Cell line data without clinical trait information - not suitable for trait association analysis"
+)
+# No linked data to save since data is not usable