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@@ -1690,3 +1690,37 @@ p3/preprocess/Gastroesophageal_reflux_disease_(GERD)/GSE77563.csv filter=lfs dif
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p3/preprocess/COVID-19/gene_data/TCGA.csv

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p3/preprocess/Generalized_Anxiety_Disorder/gene_data/GSE61672.csv

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p3/preprocess/Glioblastoma/code/GSE134470.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE134470"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE134470"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE134470.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE134470.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE134470.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info, this is a gene expression microarray study with GeneChip® Human Gene 1.0ST array
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait data is available in feature 0 - distinguishes normal brain vs GBM samples
+trait_row = 0
+
+# Age and gender not available in sample characteristics 
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[float]:
+    """Convert tissue/sample type to binary: 0 for normal, 1 for GBM"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1].lower()
+    if "normal brain" in value:
+        return 0.0
+    elif any(x in value for x in ["gbm", "tumor"]):
+        return 1.0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    return None
+
+def convert_gender(value: str) -> Optional[float]:
+    return None
+
+# 3. Save metadata - only initial filtering 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    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_df(clinical_features)
+    
+    # Save clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers (e.g. 7892501, 7892502) which are numeric and non-standard,
+# and based on the raw file format starting with ID_REF, these appear to be probe IDs
+# rather than gene symbols and will need to be mapped to gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. From previews we can see 'ID' column matches the gene expression row IDs, and 'gene_assignment' has gene symbols
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# 3. Convert probe-level measurements to gene expression data using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save gene data to file
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Clinical trial studying EGFR amplification in glioblastoma and response to gefitinib"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/code/GSE159000.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE159000"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE159000"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE159000.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE159000.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE159000.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Gene expression data is available according to dataset title ("Gene expression profiles...")
+is_gene_available = True
+
+# Trait data not explicitly available but all samples are GBM according to background
+trait_row = 0  # Use tissue type as trait indicator
+
+# Age and gender data available
+age_row = 2
+gender_row = 1
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    if "brain" in value.lower():
+        return 1  # All samples are GBM according to background
+    return None
+
+def convert_age(value): 
+    if not isinstance(value, str):
+        return None
+    try:
+        age = float(value.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    sex = value.split(': ')[1].upper()
+    if sex == 'F':
+        return 0
+    elif sex == 'M':
+        return 1
+    return None
+
+# Save initial filtering results
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=True)
+
+# Extract clinical features
+clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait,
+                                               age_row=age_row, 
+                                               convert_age=convert_age,
+                                               gender_row=gender_row,
+                                               convert_gender=convert_gender)
+
+# Preview and save clinical data
+preview_df(clinical_features)
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers start with "ILMN_" which indicates they are Illumina probe IDs
+# These are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get gene mapping based on 'ID' (probe identifier) and 'Symbol' (gene symbol)
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Normalize gene symbols to handle synonyms
+gene_data = normalize_gene_symbols_in_index(gene_data)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Clinical trial studying EGFR amplification in glioblastoma and response to gefitinib"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/code/GSE175700.py

@@ -0,0 +1,124 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE175700"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE175700"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE175700.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE175700.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE175700.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Gene data availability
+# Yes - the dataset contains microarray data for gene expression analysis
+is_gene_available = True
+
+# Clinical feature keys and conversion functions
+trait_row = None  # All samples are U87 cell lines, no trait variation
+age_row = None   # Age not available for cell line data
+gender_row = None  # All samples are male cell line, no gender variation
+
+# Since no clinical features are available with variation, we don't need conversion functions
+def convert_trait(x):
+    return None
+
+def convert_age(x): 
+    return None
+
+def convert_gender(x):
+    return None
+
+# Save metadata
+validate_and_save_cohort_info(is_final=False,
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=False)  # trait_row is None so trait data unavailable
+
+# Skip clinical feature extraction since no trait data available
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers starting with "AFFX-", these are Affymetrix probe IDs
+# They need to be mapped to standard human gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get the mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply the mapping to convert probe-level to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print dimensions to verify mapping result
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows after mapping:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Update final validation info with gene_data as df and is_biased=True
+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,  # No trait variation means completely biased
+    df=gene_data,    # Pass gene_data as df
+    note="Cell line study with no trait variation - all samples are U87 cell line"
+)

p3/preprocess/Glioblastoma/code/GSE178236.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE178236"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE178236"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE178236.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE178236.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE178236.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info mentioning "gene expression analysis" and "gene expression profile"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability 
+trait_row = 5  # IDH status can indicate glioblastoma subtype
+age_row = 2    # Age information available
+gender_row = 1 # Gender information available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    # Convert IDH status to binary: 1 for wild-type (wt), 0 for mutant (mut)
+    if 'wt' in value:
+        return 1
+    elif 'mut' in value:
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        # Extract numeric age value after colon
+        age = int(x.split(': ')[-1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].lower()
+    if 'female' in value:
+        return 0
+    elif 'male' in value:
+        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
+# Since trait_row is not None, extract clinical features
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+print("\nPreview of extracted clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Observe the identifiers start with "ILMN_" - these are Illumina probe IDs, not gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview results 
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Clinical trial studying EGFR amplification in glioblastoma and response to gefitinib"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/code/GSE226976.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE226976"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE226976"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE226976.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE226976.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE226976.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# From series title and summary, this is gene expression data for glioblastoma samples
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait data (recurrent glioma) is available in row 0
+trait_row = 0
+
+# Age and gender not available in sample characteristics
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # Extract value after colon
+    value = x.split(':')[1].strip().lower()
+    # Convert to binary - sample is recurrent glioma
+    if 'recurrent glioma' in value:
+        return 1
+    else:
+        return None
+        
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features since trait_row is not None
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row, 
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save clinical features
+print("Clinical features preview:")
+print(preview_df(clinical_features))
+
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the first 20 gene identifiers, they appear to be valid HGNC gene symbols
+# Examples like A2M, ABCF1, ACVR1C, ADAM12, ADGRE1 etc. are all recognized human gene symbols
+# No mapping needed since the data already uses standard gene symbols
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Clinical trial studying EGFR amplification in glioblastoma and response to gefitinib"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/code/GSE249289.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE249289"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE249289"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE249289.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE249289.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE249289.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene expression data availability check
+is_gene_available = True  # Based on series title and design, this is gene expression data
+
+# 2. Data availability and conversion functions
+trait_row = None  # No direct disease status as all samples are glioblastoma
+age_row = 2  # Age information is in row 2
+gender_row = 1  # Gender information is in row 1
+
+def convert_age(x):
+    try:
+        # Extract numeric value 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].lower()
+        if gender == 'female':
+            return 0
+        elif gender == 'male':
+            return 1
+        return None
+    except:
+        return None
+
+def convert_trait(x):
+    # Not used as trait data not available
+    return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract clinical features if available
+# Skip this step since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers start with "ILMN_" which indicates they are Illumina probe IDs
+# These need to be mapped to official human gene symbols for analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Get mapping dataframe with ID and Symbol columns from gene annotation
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Print shape and preview to verify the mapping result
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Extract clinical features and link with genetic data
+clinical_features = geo_select_clinical_features(
+    clinical_data, 
+    trait=trait,
+    trait_row=None, 
+    age_row=2,
+    convert_age=convert_age,
+    gender_row=1, 
+    convert_gender=convert_gender
+)
+
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, "Age")  # Use Age as primary feature since trait is not available
+
+# 4. Check for biased features and remove them if needed
+is_biased = False  # Only demographic features, no trait to be biased
+linked_data = judge_and_remove_biased_features(linked_data, "Age")[1]  # Use Age since trait is not available
+
+# 5. Validate and save cohort info
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Contains gene expression data with age and gender information, but no trait data for analysis"
+)
+
+# 6. Save linked data with demographic features
+os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Record metadata about dataset's unavailability for trait analysis
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=None,
+    df=None,
+    note="Contains gene expression data from glioblastoma tumorspheres but no control samples for trait analysis"
+)

p3/preprocess/Glioblastoma/code/GSE279426.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE279426"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE279426"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE279426.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE279426.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE279426.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this is gene expression data based on the background info
+is_gene_available = True
+
+# 2.1 Data Availability
+
+# For trait (Glioblastoma): EGFR amplification status is available and relevant 
+trait_row = 4  # egfr_amplification: A0/A1
+
+# Age and gender are not available in the data
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert EGFR amplification status to binary"""
+    if pd.isna(value) or value is None:
+        return None
+    value = value.split(': ')[-1].strip()
+    if value == 'A0':  # Not amplified
+        return 0
+    elif value == 'A1':  # Amplified
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Placeholder function since age data not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Placeholder function since gender data not available"""
+    return None
+
+# 3. Save metadata and do initial filtering
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
+                                                   age_row, convert_age,
+                                                   gender_row, convert_gender)
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Analyzing the gene identifiers
+# The identifiers (e.g. "1007_s_at", "1053_at") appear to be Affymetrix probe IDs
+# These need to be mapped to standard human gene symbols for analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# Extract gene mapping from annotation data
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview result to verify 
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Clinical trial studying EGFR amplification in glioblastoma and response to gefitinib"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/code/GSE39144.py

@@ -0,0 +1,188 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE39144"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE39144"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/GSE39144.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE39144.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE39144.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# GeneChip Human Genome U133 Plus 2.0 Array was used, indicating gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+
+# For trait: Feature 0 contains "cell type: glioma-initiating cells" which indicates glioblastoma samples
+trait_row = 0  
+
+# Age: No age information available in sample characteristics 
+age_row = None
+
+# Gender: Feature 2 contains gender information
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    # Convert glioma vs non-glioma
+    if "glioma-initiating cells" in value.lower():
+        return 1
+    return 0
+
+def convert_age(value: str) -> float:
+    # Not used but defined for completeness
+    return None
+
+def convert_gender(value: str) -> int:
+    # Extract value after colon and convert to binary
+    if ":" in value:
+        gender = value.split(":")[1].strip().lower()
+        if gender == "female":
+            return 0
+        elif gender == "male":
+            return 1
+        elif "pooled female" in gender:
+            return 0
+        elif "pooled male" in gender:
+            return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Extract clinical features since trait_row is not None
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview_result = preview_df(clinical_df)
+print("Preview of clinical features:")
+print(preview_result)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Examine the gene identifiers - these are Affymetrix probe IDs that require mapping to gene symbols
+# Format: XXXXX_at, XXXXX_s_at, XXXXX_x_at etc.
+# These are probe set IDs from Affymetrix arrays, not gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation columns and example values:")
+print(preview_df(gene_annotation))
+# 1. Identify relevant columns from gene annotation
+# 'ID' column matches probe IDs in gene expression data
+# 'Gene Symbol' column contains the gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = 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(gene_data, mapping_df)
+
+# Preview result
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols and save normalized gene data
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Gene expression data from hiPSCs, hESCs and differentiated cells, including glioblastoma cells"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/code/TCGA.py

@@ -0,0 +1,97 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Glioblastoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Glioblastoma/cohort_info.json"
+
+# 1. Select the relevant subdirectory for glioblastoma
+subdirectory = 'TCGA_Glioblastoma_(GBM)'
+cohort_dir = os.path.join(tcga_root_dir, subdirectory)
+
+# 2. Get the file paths 
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data files  
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Define candidate columns for age and gender
+candidate_age_cols = ['CDE_DxAge', 'age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Preview age and gender columns from clinical data
+age_cols_dict = clinical_df[candidate_age_cols].head(5).to_dict('list')
+print(f"Age columns preview: {age_cols_dict}")
+
+gender_cols_dict = clinical_df[candidate_gender_cols].head(5).to_dict('list') 
+print(f"Gender columns preview: {gender_cols_dict}")
+# Analyze age columns
+age_col = 'age_at_initial_pathologic_diagnosis'  # Choose this as it provides direct age values in years
+
+# Analyze gender columns 
+gender_col = 'gender'  # Only one gender column available
+
+# Print chosen columns
+print(f"Chosen age column: {age_col}")
+print(f"Chosen gender column: {gender_col}")
+# 1. Extract and standardize clinical features
+# Create trait labels from sample IDs (01-09: tumor=1, 10-19: normal=0)
+clinical_features = tcga_select_clinical_features(
+    clinical_df, 
+    trait=trait,
+    age_col='age_at_initial_pathologic_diagnosis',
+    gender_col='gender'
+)
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data on sample IDs
+linked_data = pd.merge(
+    clinical_features, 
+    normalized_gene_df.T,
+    left_index=True,
+    right_index=True,
+    how='inner'
+)
+
+# 4. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate data quality and save cohort info
+note = "Contains molecular data from tumor and normal samples with patient demographics."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Glioblastoma/gene_data/GSE134470.csv

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p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE48801.csv

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p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE50012.csv

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+Gender,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE57795.csv

@@ -0,0 +1,2 @@
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+Glucocorticoid_Sensitivity,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE58715.csv

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p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE65645.csv

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p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/GSE66705.csv

@@ -0,0 +1,2 @@
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p3/preprocess/Glucocorticoid_Sensitivity/clinical_data/TCGA.csv

@@ -0,0 +1,93 @@
+sampleID,Glucocorticoid_Sensitivity,Age,Gender
+TCGA-OR-A5J1-01,1,58,1
+TCGA-OR-A5J2-01,1,44,0
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