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p3/preprocess/Endometrioid_Cancer/code/GSE65986.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE65986"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE65986"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE65986.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE65986.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE65986.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene expression data availability
+# From background info: "Gene expression in 55 epithelial ovarian cancers ... was analyzed by Affymetrix U133plus2 array"
+is_gene_available = True  
+
+# 2.1 Data availability
+# For trait (Endometrioid_Cancer):
+# Key 0 has cancer histology types including "Endometrioid"
+trait_row = 0  
+
+# For age:
+# Key 1 has age values 
+age_row = 1
+
+# For gender:
+# No gender info in characteristics, all samples appear to be female based on ovarian cancer study
+gender_row = None
+
+# 2.2 Data type conversion functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.split(': ')[1].lower() if ': ' in x else x.lower()
+    if 'endometrioid' in x:
+        return 1
+    elif x in ['clear', 'serous']:  # Other cancer types
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(x):
+    return None  # No gender data
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed clinical data
+preview_result = preview_df(selected_clinical)
+print("Preview of processed clinical data:")
+print(preview_result)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Based on the gene expression data preview:
+# The identifiers shown are probe IDs from Affymetrix microarray platform 
+# (e.g., '1007_s_at', '1053_at' are typical Affymetrix probe formats)
+# These need to be mapped to human gene symbols for standardization
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. The 'ID' column in gene_metadata contains probe IDs (e.g., '1007_s_at') matching the gene expression data indices,
+#    and 'Gene Symbol' column contains the corresponding gene symbols
+prob_col = 'ID'  
+gene_col = 'Gene Symbol'
+
+# 2. Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview first few rows
+print("Gene expression data shape:", gene_data.shape)
+print("\nPreview of first few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
+)
+
+# 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/Endometrioid_Cancer/code/GSE66667.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE66667"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE66667"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE66667.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE66667.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE66667.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on background info mentioning "Microarrays" and "global transcription", 
+# this appears to be gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait can be determined from "histology" field
+trait_row = 0
+
+# Age and gender are not available in the sample characteristics
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert histology values to binary for Endometrioid cancer"""
+    if not isinstance(value, str):
+        return None
+    # Extract value after colon and strip whitespace
+    if ":" in value:
+        value = value.split(":", 1)[1].strip()
+    # Convert to binary where Endometrioid = 1, others = 0
+    return 1 if value == "Endometrioid" else 0
+
+def convert_age(value: str) -> float:
+    return None  # Age data not available
+
+def convert_gender(value: str) -> int:
+    return None  # Gender data not available
+
+# 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=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we proceed with clinical feature extraction
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the clinical features
+print("Preview of clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Check gene identifiers format
+# The IDs like '1007_s_at', '1053_at' etc. are Affymetrix probe IDs
+# These need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get gene mapping from annotation data
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview data
+print("Shape of gene expression data:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
+)
+
+# 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/Endometrioid_Cancer/code/GSE68600.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE68600"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE68600"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE68600.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE68600.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE68600.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# Gene expression data availability - Yes, as indicated by !Series_title about gene expression data and Affymetrix array
+is_gene_available = True
+
+# Variable availability
+# Trait - Row 4 contains histological types. Endometrioid type indicates trait presence
+trait_row = 4
+
+# Age - Not available in sample characteristics 
+age_row = None 
+
+# Gender - Row 0 contains gender info but all samples are female (F), so not useful
+gender_row = None
+
+def convert_trait(value):
+    """Convert histology type to binary for endometrioid cancer"""
+    if pd.isna(value) or not isinstance(value, str):
+        return None
+    value = value.lower().split(": ")[-1]
+    # Positive if endometrioid is mentioned in histology
+    if "endometrioid" in value:
+        return 1
+    # Other histology types are negative
+    return 0
+
+# Age conversion function not needed since age data unavailable
+convert_age = None
+
+# Gender conversion function not needed since all samples are female
+convert_gender = None
+
+# Save metadata - is_trait_available determined by trait_row being not None
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Extract clinical features since trait data is available
+clinical_df = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview extracted clinical data
+preview_dict = preview_df(clinical_df)
+print("Preview of clinical data:")
+print(preview_dict)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The identifiers like 'A28102_at', 'AB000114_at' etc. appear to be Affymetrix probe IDs
+# rather than human gene symbols. These will need to be mapped to standard gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get gene mapping dataframe from annotation data
+# 'ID' stores probe IDs matching gene expression data
+# 'Gene Symbol' stores corresponding gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape of gene expression data after mapping
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of first few rows and columns:")
+print(gene_data.iloc[:5, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
+)
+
+# 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/Endometrioid_Cancer/code/GSE73551.py

@@ -0,0 +1,150 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73551"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73551"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE73551.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE73551.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE73551.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background information showing solid tumor gene expression analysis, 
+# this dataset likely contains gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (Endometrioid Cancer) can be inferred from cell type in key 0
+trait_row = 0  
+
+# Age and gender not recorded in characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    # Extract value after colon if present
+    if ':' in value:
+        value = value.split(':', 1)[1].strip()
+    # Convert to binary - 1 for endometrioid, 0 for other cancer types
+    return 1 if value.upper() == 'ENDOMETRIOID' else 0
+
+# Since age/gender not available, their conversion functions not needed
+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. Clinical Feature Extraction
+# Since trait_row is not None, proceed with extraction
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed clinical data
+print("Preview of processed clinical data:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The row IDs are numeric indices (1, 2, 3, etc.) rather than human gene symbols or probe IDs,
+# so gene mapping is required
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract gene mapping information from annotation data
+# 'ID' in gene_metadata matches the numeric indices in genetic_df
+# 'GeneSymbol' contains the human gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GeneSymbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview the mapped gene expression data
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
+)
+
+# 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/Endometrioid_Cancer/code/GSE73614.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73614"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE73614.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE73614.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE73614.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the series summary mentioning "transcriptional profile" and "gene expression signatures",
+# this dataset appears to contain gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# We cannot reliably determine case/control status from tissue field, so trait data is not available
+trait_row = None
+age_row = None  
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    if value is None:
+        return None
+    val = value.split(": ")[-1].strip().lower()
+    if "endometrioid" in val:
+        return 1
+    elif val in ["healthy", "normal", "benign"]:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    if value is None:
+        return None
+    val = value.split(": ")[-1].strip()
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    if value is None:
+        return None
+    val = value.split(": ")[-1].strip().lower()
+    if val in ["female", "f"]:
+        return 0
+    elif val in ["male", "m"]:
+        return 1
+    return None
+
+# 3. Save Metadata
+# Initial filtering - trait data not available
+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
+# Skip since trait_row is None
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# These appear to be Agilent probe IDs (e.g. A_23_P100001) rather than gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# 1. From the preview, we can see that 'ID' contains probe IDs like A_23_P100001
+# and 'GENE_SYMBOL' contains human gene symbols
+
+# 2. Get mapping between probe IDs and gene symbols
+gene_mapping = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, gene_mapping)
+
+# Preview results
+print("Gene expression data shape:", gene_data.shape)
+print("\nFirst few genes and samples:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+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)
+
+# Final validation with the gene expression 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,  # No trait data means biased for our purpose
+    df=gene_data,
+    note="Gene expression data available but no trait information could be extracted"
+)

p3/preprocess/Endometrioid_Cancer/code/GSE73637.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73637"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73637"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE73637.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE73637.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE73637.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# From series title and design, this appears to be gene expression data from cell lines
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Rows
+# Trait (Endometrioid) can be determined from histopathology in row 3
+trait_row = 3
+
+# Age and gender not available for cell lines
+age_row = None  
+gender_row = None
+
+# 2.2 Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert histopathology to binary trait"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'endometrioid' in value:
+        return 1
+    # For cases where we can be sure it's not endometrioid
+    if any(x in value for x in ['serous', 'clear cell', 'undifferentiated']):
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Placeholder function since age data not available"""
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Placeholder function since gender data not available"""
+    return None
+
+# 3. Save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. 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 = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# Given that the row identifiers are simply numerical indices (1, 2, 3, etc) rather than 
+# recognizable gene symbols like BRCA1, TP53, etc., we need to perform gene mapping 
+requires_gene_mapping = True
+# Extract gene annotation data with proper handling
+filtered_lines = []
+with gzip.open(soft_file, 'rt') as f:
+    for line in f:
+        if not any(line.startswith(prefix) for prefix in ['^', '!', '#']):
+            filtered_lines.append(line.strip())
+
+# Preview the structure of filtered lines
+print("Sample of filtered lines:")
+for line in filtered_lines[:5]:
+    print(line)
+
+if filtered_lines:
+    # Try to create DataFrame from filtered lines
+    try:
+        df_text = '\n'.join(filtered_lines)
+        gene_metadata = pd.read_csv(io.StringIO(df_text), sep='\t', 
+                                  engine='python', on_bad_lines='skip')
+        print("\nColumn names:")
+        print(gene_metadata.columns)
+        print("\nPreview:")
+        print(preview_df(gene_metadata))
+    except Exception as e:
+        print(f"Error creating DataFrame: {str(e)}")
+# The gene expression data uses numerical IDs that match the 'ID' column in gene annotation
+# The 'GeneSymbol' column contains the gene symbols we want to map to
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GeneSymbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print shape and preview to verify the mapping
+print("Gene expression data shape:", gene_data.shape)
+print("\nPreview of first few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
+)
+
+# 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/Endometrioid_Cancer/code/GSE94523.py

@@ -0,0 +1,142 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE94523"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94523"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE94523.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE94523.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE94523.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# Check gene availability
+# From series title and summary, we can see it's a microarray expression dataset
+is_gene_available = True
+
+# Analyze the availability of variables
+# The dictionary shows all samples are "endometrioid adenocarcinoma" in row 0
+# This can be used as trait data (binary: case vs control), all samples are cases
+trait_row = 0
+
+# No age or gender information available 
+age_row = None
+gender_row = None
+
+# Define conversion functions
+def convert_trait(value: str) -> int:
+    """Convert trait values to binary (0: control, 1: case)"""
+    if "endometrioid adenocarcinoma" in value.lower():
+        return 1
+    elif value.strip() == "":
+        return None
+    return 0
+
+# No conversion functions needed for unavailable data
+convert_age = None  
+convert_gender = None
+
+# Save metadata about data availability
+is_usable = validate_and_save_cohort_info(is_final=False, 
+                                         cohort=cohort,
+                                         info_path=json_path,
+                                         is_gene_available=is_gene_available,
+                                         is_trait_available=(trait_row is not None))
+
+# Extract clinical features if available
+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
+    preview = preview_df(selected_clinical_df)
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The row IDs are numbers (non-gene identifiers) which will need to be mapped to actual gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Extract gene mapping information using ID and HUGO columns
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='HUGO')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Preview mapping results
+print("Original data shape:", genetic_df.shape)
+print("Mapped data shape:", gene_data.shape)
+print("\nFirst few rows and columns of mapped data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
+)
+
+# 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/Endometrioid_Cancer/code/GSE94524.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE94524"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE94524.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE94524.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE94524.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# Get paths to the 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 feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# Step 1: Gene Expression Data Availability
+# From title, this appears to be a gene expression dataset studying tamoxifen-associated endometrial tumors
+is_gene_available = True
+
+# Step 2: Variable Availability and Data Type Conversion
+# From characteristics dict, all samples are endometrioid adenocarcinoma (trait=1)
+trait_row = 0  
+age_row = None  # Age data not available
+gender_row = None  # Gender data not available, but since endometrial cancer, we know all patients are female
+
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary (0 for normal/control, 1 for endometrioid cancer)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'endometrioid' in value and 'adenocarcinoma' in value:
+        return 1
+    return None
+
+# Age conversion function not needed since age data unavailable
+convert_age = None
+
+# Gender conversion function not needed since gender data unavailable
+convert_gender = None
+
+# Step 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
+)
+
+# Step 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 selected features
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:")
+    print(preview)
+    
+    # Save clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The gene identifiers appear to be just row numbers (1, 2, 3, etc.)
+# This indicates they need to be mapped to actual human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# From the preview, we can see that 'ID' column matches the gene expression row IDs,
+# and 'HUGO' column contains the gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='HUGO')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_data)
+
+# Preview the mapped gene expression data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+print("\nPreview of first few rows and columns:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
+)
+
+# 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/Endometrioid_Cancer/code/TCGA.py

@@ -0,0 +1,80 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometrioid_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Endometrioid_Cancer/cohort_info.json"
+
+# First list available directories to verify
+print("Available directories in TCGA root:")
+tcga_dirs = os.listdir(tcga_root_dir)
+print(tcga_dirs)
+
+# Get Endometrioid Cancer cohort directory path (UCEC = Uterine Corpus Endometrial Carcinoma)
+cohort_name = "TCGA.UCEC.sampleMap" # Directory containing endometrial cancer data
+cohort_dir = os.path.join(tcga_root_dir, cohort_name)
+
+# Get clinical and genetic file paths 
+clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load clinical and genetic data
+clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
+genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)
+
+# Print clinical data columns for analysis
+print("\nClinical data columns:")
+print(clinical_df.columns.tolist())
+
+# Record data availability in metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path, 
+    is_gene_available=len(genetic_df.columns) > 0,
+    is_trait_available=True # Since we found the endometrial cancer directory
+)
+# First verify the root directory exists and print contents
+print(f"TCGA root directory path: {tcga_root_dir}")
+if os.path.exists(tcga_root_dir):
+    print("Directory exists. Contents:", os.listdir(tcga_root_dir))
+    
+    # Get Endometrioid Cancer cohort directory path
+    cohort_dir = os.path.join(tcga_root_dir, "TCGA_Endometrioid_Cancer_(UCEC)")
+
+    # Get clinical and genetic file paths
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load clinical and genetic data
+    clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
+    genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)
+
+    # Print clinical data columns for analysis
+    print("\nClinical data columns:")
+    print(clinical_df.columns.tolist())
+
+    # Record data availability in metadata
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=len(genetic_df.columns) > 0,
+        is_trait_available=True  # Since we're using the endometrial cancer directory
+    )
+else:
+    print("Directory does not exist")
+    # Record unavailability in metadata
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )

p3/preprocess/Endometrioid_Cancer/gene_data/GSE120490.csv

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p3/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv

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p3/preprocess/Endometrioid_Cancer/gene_data/GSE73614.csv

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p3/preprocess/Endometrioid_Cancer/gene_data/GSE73637.csv

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p3/preprocess/Endometrioid_Cancer/gene_data/GSE94523.csv

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p3/preprocess/Endometrioid_Cancer/gene_data/GSE94524.csv

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p3/preprocess/Endometriosis/GSE145701.csv

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p3/preprocess/Endometriosis/GSE145702.csv

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p3/preprocess/Endometriosis/GSE51981.csv

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p3/preprocess/Endometriosis/clinical_data/GSE111974.csv

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p3/preprocess/Endometriosis/clinical_data/GSE120103.csv

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p3/preprocess/Endometriosis/clinical_data/GSE138297.csv

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p3/preprocess/Endometriosis/clinical_data/GSE145701.csv

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p3/preprocess/Endometriosis/clinical_data/GSE145702.csv

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p3/preprocess/Endometriosis/clinical_data/GSE165004.csv

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+,GSM5024320,GSM5024321,GSM5024322,GSM5024323,GSM5024324,GSM5024325,GSM5024326,GSM5024327,GSM5024328,GSM5024329,GSM5024330,GSM5024331,GSM5024332,GSM5024333,GSM5024334,GSM5024335,GSM5024336,GSM5024337,GSM5024338,GSM5024339,GSM5024340,GSM5024341,GSM5024342,GSM5024343,GSM5024344,GSM5024345,GSM5024346,GSM5024347,GSM5024348,GSM5024349,GSM5024350,GSM5024351,GSM5024352,GSM5024353,GSM5024354,GSM5024355,GSM5024356,GSM5024357,GSM5024358,GSM5024359,GSM5024360,GSM5024361,GSM5024362,GSM5024363,GSM5024364,GSM5024365,GSM5024366,GSM5024367,GSM5024368,GSM5024369,GSM5024370,GSM5024371,GSM5024372,GSM5024373,GSM5024374,GSM5024375,GSM5024376,GSM5024377,GSM5024378,GSM5024379,GSM5024380,GSM5024381,GSM5024382,GSM5024383,GSM5024384,GSM5024385,GSM5024386,GSM5024387,GSM5024388,GSM5024389,GSM5024390,GSM5024391
+Endometriosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Endometriosis/clinical_data/GSE37837.csv

@@ -0,0 +1,3 @@
+,GSM928779,GSM928780,GSM928781,GSM928782,GSM928783,GSM928784,GSM928785,GSM928786,GSM928787,GSM928788,GSM928789,GSM928790,GSM928791,GSM928792,GSM928793,GSM928794,GSM928795,GSM928796,GSM928797,GSM928798,GSM928799,GSM928800,GSM928801,GSM928802,GSM928803,GSM928804,GSM928805,GSM928806,GSM928807,GSM928808,GSM928809,GSM928810,GSM928811,GSM928812,GSM928813,GSM928814
+Endometriosis,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0
+Age,29.0,29.0,40.0,40.0,33.0,33.0,45.0,45.0,24.0,24.0,38.0,38.0,28.0,28.0,25.0,25.0,40.0,40.0,31.0,31.0,37.0,37.0,30.0,30.0,30.0,30.0,37.0,37.0,31.0,31.0,34.0,34.0,25.0,25.0,40.0,40.0

p3/preprocess/Endometriosis/clinical_data/GSE51981.csv

@@ -0,0 +1,2 @@
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p3/preprocess/Endometriosis/clinical_data/GSE73622.csv

@@ -0,0 +1,3 @@
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p3/preprocess/Endometriosis/clinical_data/GSE75427.csv

@@ -0,0 +1,3 @@
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p3/preprocess/Endometriosis/clinical_data/TCGA.csv

@@ -0,0 +1,597 @@
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p3/preprocess/Endometriosis/code/GSE111974.py

@@ -0,0 +1,122 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE111974"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE111974"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometriosis/GSE111974.csv"
+out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE111974.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE111974.csv"
+json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on series title and summary mentioning "RNA expression", and focusing on endometrial tissue
+is_gene_available = True
+
+# 2. Trait, Age, Gender Data Analysis
+# 2.1 Data Availability
+# trait row: We can infer endometriosis status from being in RIF vs control group
+trait_row = 0  
+
+# Age and gender not explicitly available in sample characteristics
+age_row = None
+gender_row = None 
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert RIF/Control status to binary
+    From background: RIF group = cases, Fertile control = controls"""
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(": ")[-1]
+    if "endometrial tissue" in value:
+        # Here we can't determine case/control status from this field alone
+        return None
+    return None
+
+def convert_age(value):
+    return None  # Age data not available
+
+def convert_gender(value): 
+    return None  # Gender data not available
+
+# 3. Save Initial Metadata
+is_trait_available = trait_row is not None
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract Clinical Features (skip since trait conversion gives None)
+if trait_row is not None:
+    clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview results
+    print(preview_df(clinical_df))
+    
+    # Save to CSV
+    clinical_df.to_csv(out_clinical_data_file)
+# 1. Gene Expression Data: No gene expression data found
+is_gene_available = False
+
+# 2. Variable availability and conversion
+trait_row = None  # No trait information available
+age_row = None  # No age information available
+gender_row = None  # No gender information available
+
+# No conversion functions needed since no data is available
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Skip clinical feature extraction since trait_row is None

p3/preprocess/Endometriosis/code/GSE120103.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE120103"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE120103"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometriosis/GSE120103.csv"
+out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE120103.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE120103.csv"
+json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene expression data is available as it's a whole genome expression microarray
+is_gene_available = True
+
+# 2.1 Data availability
+trait_row = 1  # The sample group field contains endometriosis status
+age_row = None  # Age information not available
+gender_row = 0  # Gender information available but constant (all female)
+
+# 2.2 Data type conversion functions
+def convert_trait(x):
+    if not x or ':' not in x:
+        return None
+    value = x.split(': ')[1].strip().lower()
+    # Convert based on presence of "disease free" in the description
+    if "disease free" in value:
+        return 0  # No endometriosis
+    elif "stage iv ovarian endometriosis" in value:
+        return 1  # Has endometriosis
+    return None
+
+def convert_age(x):
+    # Not used since age data is not available
+    return None
+
+def convert_gender(x):
+    # Not used since all samples are female
+    return None
+
+# 3. Save metadata 
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the gene identifiers (e.g. 'A_23_P100001'), these appear to be Agilent probe IDs
+# They need to be mapped to human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract probe ID and gene symbol columns for mapping
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview the mapped gene expression data
+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
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+trait_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=trait_biased,
+    df=linked_data,
+    note="Study examining expression profiles in endometriotic cyst stromal cells versus normal endometrial stromal cells."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Endometriosis/code/GSE138297.py

@@ -0,0 +1,138 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometriosis"
+cohort = "GSE138297"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometriosis"
+in_cohort_dir = "../DATA/GEO/Endometriosis/GSE138297"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Endometriosis/GSE138297.csv"
+out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE138297.csv"
+out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE138297.csv"
+json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability 
+is_gene_available = False  # Gene expression data measures IBS response, not suitable for Endometriosis study
+
+# 2.1 Data Availability
+trait_row = None  # No Endometriosis data available in this IBS study
+age_row = 3  # Age data in years
+gender_row = 1  # Gender data encoded as binary
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    return None  # Not needed since trait data is unavailable
+
+def convert_age(value):
+    if value is None:
+        return None
+    try:
+        return float(value.split(': ')[-1].strip())
+    except:
+        return None
+
+def convert_gender(value):
+    if value is None:
+        return None
+    try:
+        # Value is already encoded as we want (female=1, male=0)
+        return int(value.split(': ')[-1].strip())  
+    except:
+        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
+# Skip since trait_row is None
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The gene identifiers are numeric IDs starting with 16650xxx
+# These are not standard human gene symbols and need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Get file paths and load initial gene expression data
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+gene_data = get_genetic_data(matrix_file)
+
+# Get gene mapping dataframe from annotation data
+# 'ID' column in metadata matches IDs in expression data
+# 'gene_assignment' contains gene symbols, but needs parsing
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
+
+# Apply gene mapping to expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Normalize gene symbols to official ones
+gene_data = normalize_gene_symbols_in_index(gene_data)
+# 1. Save normalized gene data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. 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=False,  # Changed to False since trait data isn't available
+    is_biased=None,  # Not applicable since trait isn't available
+    df=None,  # No linked data to provide
+    note="Dataset contains gene expression data but lacks endometriosis trait information."
+)