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p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE200306.csv

@@ -0,0 +1,4 @@
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p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE112943.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE112943"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE112943.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE112943.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The series title and design indicate microarray gene expression analyses
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# For trait - row 0 contains tissue type indicating disease vs control
+trait_row = 0
+# Age and gender not available in sample characteristics
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert tissue type to binary trait"""
+    if value is None:
+        return None
+    value = value.split(": ")[1].strip()
+    # CCLE and SCLE are lupus cases, others are controls
+    if value in ['CCLE', 'SCLE', 'Lupus Nephritis']:
+        return 1
+    elif value in ['Skin Control', 'Kidney Control']:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float - not used since age not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary - not used since gender not available"""
+    return None
+
+# 3. Save Metadata 
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Create directories if they don't exist
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers are Illumina probe IDs (starting with "ILMN_")
+# These need to be mapped to human gene symbols for proper analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. From observation, 'ID' in gene annotation matches the probe IDs in gene expression data,
+# and 'Symbol' contains the gene symbols
+
+# 2. Get gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the result
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nPreview of gene data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains skin biopsy gene expression data comparing childhood-onset SLE patients with healthy controls."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE131528.py

@@ -0,0 +1,170 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE131528"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE131528"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE131528.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE131528.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE131528.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Looking at background info, this appears to be about T1D and biomarkers
+# No indication of gene expression data, appears to focus on immune signatures and clinical metrics
+is_gene_available = False
+
+# 2.1 Data Availability
+# trait: T1D status is available in row 1
+trait_row = 1
+
+# age: Available in rows 3 and 4 in different formats
+age_row = 3 
+
+# gender: Available in rows 4 and 5 
+gender_row = 4
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    # Split by colon and get the value
+    val = x.split(': ')[-1].strip()
+    if 'T1D' in val:
+        return 1
+    return 0
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    # Extract numeric value after "age at enrollment:"
+    val = x.split(': ')[-1].strip()
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    val = x.split(': ')[-1].strip().lower()
+    if 'f' in val:
+        return 0
+    elif 'm' in val:
+        return 1
+    return None
+
+# 3. Save initial metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract clinical features since trait_row is not None
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview = preview_df(clinical_features)
+print("Preview of clinical features:")
+print(preview)
+
+# Save clinical features
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The identifiers in the gene expression data are Affymetrix probe IDs (e.g. '1007_s_at', '1053_at')
+# These need to be mapped to human gene symbols for proper analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Create mapping dataframe from probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level data to gene-level expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Display info about the result
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains skin biopsy gene expression data comparing childhood-onset SLE patients with healthy controls."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE148810.py

@@ -0,0 +1,163 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE148810"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE148810.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE148810.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE148810.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Series_overall_design mentions "Affymetrix ST 2.1 microarray analysis", so gene expression data is available
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait data (SLE vs control) can be found in disease field (key 1)
+trait_row = 1
+# Age and gender data not available in any fields
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    # Extract value after colon
+    if ':' in value:
+        value = value.split(':', 1)[1].strip()
+    # Convert to binary (1 for SLE, 0 for control/normal)    
+    if 'cSLE' in value:
+        return 1
+    elif 'Normal' in value:
+        return 0
+    # Ignore JM samples
+    return None
+
+def convert_age(value: str) -> float:
+    return None  # Not used since age data not available
+
+def convert_gender(value: str) -> int:
+    return None  # Not used since gender data not available
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                                   trait=trait,
+                                                   trait_row=trait_row,
+                                                   convert_trait=convert_trait,
+                                                   age_row=age_row,
+                                                   convert_age=convert_age, 
+                                                   gender_row=gender_row,
+                                                   convert_gender=convert_gender)
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_features.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers end with "_at" which indicates they are probe IDs from an Affymetrix microarray
+# These need to be mapped to human gene symbols for standardization
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Identify columns for mapping
+# 'ID' in annotation corresponds to gene identifiers in expression data
+# 'ORF' contains the gene symbols
+prob_col = 'ID'
+gene_col = 'ORF'
+
+# 2. Get mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, gene_mapping)
+
+# Print shapes to verify the conversion
+print("Shape before mapping (probes):", genetic_data.shape)
+print("Shape after mapping (genes):", gene_data.shape)
+
+# Preview first few gene symbols and expression values
+print("\nPreview of gene expression data:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset contains skin biopsy gene expression data comparing childhood-onset SLE patients with healthy controls."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE154851.py

@@ -0,0 +1,130 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE154851"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE154851"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE154851.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE154851.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE154851.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# The background information mentions using "Human Gene Expression Microarray kit"
+# and gene expression analysis, so gene expression data is available
+is_gene_available = True
+
+# 2. Data Availability and Type Conversion
+# For trait: Not directly available in characteristics
+def convert_trait(x):
+    if x is None:
+        return None
+    value = x.split(": ")[-1].lower()
+    # Based on series design description - SLE patients vs healthy controls 
+    return 1 if "sle" in value or "lupus" in value else 0
+
+# For gender: Available in row 1
+def convert_gender(x):
+    if x is None:
+        return None
+    value = x.split(": ")[-1].lower()
+    return 1 if "male" in value else 0
+
+# For age: Available in row 2
+def convert_age(x):
+    if x is None:
+        return None
+    try:
+        # Extract numeric value before 'y'
+        return float(x.split(": ")[-1].replace('y',''))
+    except:
+        return None
+
+# Set row indices based on data availability
+trait_row = None  # No explicit disease status field
+age_row = 2
+gender_row = 1
+
+# 3. Save metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=False)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# Based on the preview, the gene identifiers are simple numeric values (1, 2, 3, etc)
+# These appear to be array probe IDs rather than human gene symbols
+# Therefore mapping to gene symbols will be required
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# 1. Identify mapping columns: 'ID' matches probe identifiers, 'GENE_SYMBOL' contains gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Map probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Print shape to verify 
+print("\nGene expression data shape after mapping:")
+print(gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Exit early as trait data is not available
+note = "Dataset contains gene expression data but lacks clear trait labels in clinical annotations."
+validate_and_save_cohort_info(
+    is_final=False,  # Changed from True to False for early exit
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=False,
+    is_biased=None,
+    df=None,
+    note=note
+)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE165004.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE165004"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE165004"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE165004.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE165004.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE165004.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data from endometrial tissue RNA
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Trait, age and gender data availability
+trait_row = 0  # "subject status/group" contains trait information about patient vs control
+age_row = None  # Age information not available 
+gender_row = None  # No gender info needed as all subjects are female
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert trait values to binary: Control=0, Patient (RPL or UIF)=1"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].lower()
+    if 'control' in value:
+        return 0
+    elif 'patient' in value:
+        return 1
+    return None
+
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata about data availability
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=(trait_row is not None))
+
+# 4. Extract clinical features since trait_row is not None
+selected_clinical_df = geo_select_clinical_features(clinical_data, trait,
+                                                  trait_row=trait_row,
+                                                  convert_trait=convert_trait,
+                                                  age_row=age_row,
+                                                  convert_age=convert_age, 
+                                                  gender_row=gender_row,
+                                                  convert_gender=convert_gender)
+
+# Preview the clinical data
+print(preview_df(selected_clinical_df))
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Examine data structure
+print("Data structure and head:")
+print(genetic_data.head())
+
+print("\nShape:", genetic_data.shape)
+
+print("\nFirst 20 row IDs (gene/probe identifiers):")
+print(list(genetic_data.index)[:20])
+
+# Get a few column names to verify sample IDs
+print("\nFirst 5 column names:")
+print(list(genetic_data.columns)[:5])
+# The gene identifiers in this dataset are numeric IDs. These do not appear to be human gene symbols 
+# and will need to be mapped to standard symbols for biological interpretation.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Display column names and preview data
+print("Column names:")
+print(gene_annotation.columns)
+
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_annotation))
+# Get gene mapping from annotation data
+# ID column contains probe identifiers matching gene expression data
+# GENE_SYMBOL column contains the target gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Save gene expression data 
+gene_data.to_csv(out_gene_data_file)
+
+# Preview results
+print("\nGene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few gene symbols and their expression values:")
+print(preview_df(gene_data))
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and information saving
+note = "Dataset appears to be from endometrial tissue RNA expression study comparing patients with recurrent pregnancy losses and unexplained infertility vs controls."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180393.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE180393"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE180393"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE180393.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE180393.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE180393.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+
+# Get dictionary of unique values per row 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+for row, values in unique_values_dict.items():
+    print(f"\n{row}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on the background information mentioning microarray profiling on Affymetrix ST2.1 platform
+is_gene_available = True
+
+# 2.1 Data Availability
+# From Sample Characteristics:
+# - Trait (SLE status) can be inferred from sample group field (row 0) which shows LN vs non-LN cases
+trait_row = 0
+# Age and gender are not available in the sample characteristics
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert sample group to binary SLE status (0: non-SLE, 1: SLE)"""
+    if not isinstance(value, str):
+        return None
+    value = value.split(': ')[-1].strip()
+    # LN (Lupus Nephritis) cases indicate SLE
+    if value.startswith('LN-WHO'):
+        return 1
+    # All other cases are non-SLE controls
+    elif value in ['Living donor', 'infection-associated GN', 'FSGS', 'DN', 
+                  'amyloidosis', 'Membrano-Proliferative GN', 'MN', 'AKI',
+                  'FGGS', "2'FSGS", 'Thin-BMD', 'Immuncomplex GN', 'IgAN',
+                  'chronic Glomerulonephritis (GN) with infiltration by CLL',
+                  'CKD with mod-severe Interstitial fibrosis', 'Fibrillary GN',
+                  'Interstitial nephritis', 'Hypertensive Nephrosclerosis',
+                  'Unaffected parts of Tumor Nephrectomy']:
+        return 0
+    return None
+
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata 
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file)
+# Get gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs (gene/probe identifiers)
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index)[:20])
+# Looking at the identifiers which end in '_at', these appear to be Affymetrix probe IDs
+# rather than standard human gene symbols. These will need mapping.
+requires_gene_mapping = True
+# Extract gene annotation data - use fewer prefixes to avoid filtering out annotation data
+annotation_data = filter_content_by_prefix(soft_file_path, prefixes_a=['!'], unselect=True, source_type='file', return_df_a=True)[0]
+
+# Print all column names first
+print("All columns in annotation data:")
+print(annotation_data.columns.tolist())
+
+# Preview column names and first few values
+print("\nGene annotation preview:")
+print(preview_df(annotation_data))
+# Extract gene annotation data 
+annotation_data = get_gene_annotation(soft_file_path)
+
+# Print all column names
+print("All columns in annotation data:")
+print(annotation_data.columns.tolist())
+
+# Preview column names and first few values
+print("\nGene annotation preview:")
+print(preview_df(annotation_data))
+# Extract gene mapping from annotation data (probe ID to Entrez ID)
+mapping_df = get_gene_mapping(annotation_data, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+# Load NCBI gene ID to symbol mapping
+with open("./metadata/gene_id_to_symbol.json", "r") as f:
+    entrez_to_symbol = json.load(f)
+
+# Add gene symbol column by mapping Entrez IDs
+mapping_df['Gene'] = mapping_df['Gene'].map(lambda x: entrez_to_symbol.get(x))
+
+# Drop rows where gene symbol mapping failed
+mapping_df = mapping_df.dropna()
+
+# Apply gene mapping to convert probe expression to gene expression
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview the first few gene IDs and values
+print("\nFirst 20 genes after mapping:")
+print(list(gene_data.index)[:20])
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Drop rows where gene symbol mapping failed
+genetic_data = get_genetic_data(matrix_file_path)
+mapping_df = get_gene_mapping(annotation_data, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+# Debug gene mapping process
+print("Number of probes in mapping:", len(mapping_df))
+print("Number of probes in gene data:", len(genetic_data))
+print("Number of overlapping probes:", len(set(mapping_df['ID']).intersection(genetic_data.index)))
+
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+print("Shape of gene data after mapping:", gene_data.shape)
+print("First few genes after mapping:", list(gene_data.index)[:5])
+
+# 1. Normalize gene symbols using NCBI data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+print("Shape of gene data after normalization:", gene_data.shape)
+print("First few normalized genes:", list(gene_data.index)[:5])
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+print("\nShape of clinical data:", selected_clinical.shape)
+print("Clinical data index:", selected_clinical.index.tolist())
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+print("Shape of linked data:", linked_data.shape)
+print("Linked data columns:", linked_data.columns[:5].tolist())
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+print("Shape after handling missing values:", linked_data.shape)
+
+# 4. Check for biased features
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Save validation info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note="Dataset uses microarray probes that were mapped to genes. Age and gender info not available."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE180394.py

@@ -0,0 +1,129 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE180394"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE180394"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE180394.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE180394.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE180394.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Step 1: Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Step 2: Extract background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Step 3: Get dictionary of unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Step 4: Print background info and sample characteristics
+print("Dataset Background Information:")
+print("-" * 80)
+print(background_info)
+print("\nSample Characteristics:")
+print("-" * 80)
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on the background info, this dataset uses Affymetrix microarray for gene expression profiling
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability 
+# Trait (Lupus/SLE) can be determined from sample group in row 0
+trait_row = 0
+# Age and gender info not available in characteristics
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert trait value to binary (0: control, 1: lupus)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    # Living donor and tumor nephrectomy are controls
+    if 'living donor' in value or 'tumor nephrectomy' in value:
+        return 0
+    # LN = Lupus Nephritis cases
+    elif 'ln-who' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age value to float"""
+    return None  # Not used since age data not available
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary (0: female, 1: male)"""
+    return None  # Not used since gender data not available
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.to_csv(out_clinical_data_file)
+# 1. Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# 2. Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(genetic_data.index[:20])
+# Gene identifiers contain "_at" suffix which indicates they are probe IDs from the Affymetrix platform
+# These need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# 1. Extract gene annotation data from SOFT file
+# Try to get gene symbols from platform table section
+gene_annotation = None
+with gzip.open(soft_file_path, 'rt') as f:
+    platform_start = False
+    header = None
+    data = []
+    for line in f:
+        if '!Platform_table_begin' in line:
+            platform_start = True
+            continue
+        elif '!Platform_table_end' in line:
+            break
+        elif platform_start:
+            if header is None:
+                header = line.strip().split('\t') 
+            else:
+                data.append(line.strip().split('\t'))
+
+if data:
+    gene_annotation = pd.DataFrame(data, columns=header)
+
+# 2. Preview annotation data columns and check for gene symbol info
+if gene_annotation is not None:
+    print("Available columns in annotation data:")
+    print(gene_annotation.columns)
+    print("\nSample of annotation data:")
+    print(preview_df(gene_annotation))
+else:
+    print("No platform table data found in SOFT file")

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE184989.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE184989"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE184989"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE184989.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE184989.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE184989.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Step 1: Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Step 2: Extract background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Step 3: Get dictionary of unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Step 4: Print background info and sample characteristics
+print("Dataset Background Information:")
+print("-" * 80)
+print(background_info)
+print("\nSample Characteristics:")
+print("-" * 80)
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes, based on the background info mentioning "microarray experiment" and "gene expressions"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion 
+# 2.1 Data Availability
+# Disease state is in row 0, with multiple values including ACLE, SCLE, DLE vs NN (normal)
+trait_row = 0
+
+# Age and gender not available in characteristics
+age_row = None 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    x = x.split(": ")[-1].strip().upper()
+    # Convert to binary - any lupus type vs normal
+    if x == "NN":
+        return 0 
+    elif x in ["DLE", "SCLE", "ACLE"]:
+        return 1
+    return None
+
+def convert_age(x):
+    return None  # Not used since age not available
+
+def convert_gender(x):
+    return None  # Not used since gender not available
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row, 
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print("Preview of clinical features:")
+print(preview_df(clinical_df))
+
+# Save to CSV
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# 1. Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# 2. Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(genetic_data.index[:20])
+# These appear to be microarray probe IDs, not human gene symbols
+# The format '16650XXX' is characteristic of probe IDs that need to be mapped to gene symbols
+requires_gene_mapping = True
+# 1. Extract gene annotation data from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# 2. Preview annotation data
+print("Column names and first few values in gene annotation data:")
+print(preview_df(gene_annotation))
+
+# Preview additional rows to check for gene annotations
+print("\nPreview of rows 100-105:")
+print(preview_df(gene_annotation.iloc[100:105]))
+# 1. Get gene mapping
+# Looking at outputs, we can see:
+# - gene identifiers in gene expression data are numeric strings like '16650001'
+# - 'ID' column in gene annotation data matches these identifiers
+# - 'gene_assignment' column contains gene symbols
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')
+
+# 2. Apply gene mapping to convert probe data to gene data 
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview results
+print("\nFirst few rows of gene_data:")
+print(preview_df(gene_data))
+
+# Save intermediate gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = pd.concat([clinical_df, normalized_gene_data], axis=0).T
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove biased demographic ones
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata 
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from kidney biopsies of lupus nephritis patients. Trait is based on treatment response (responders vs non-responders)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE193442.py

@@ -0,0 +1,76 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE193442"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE193442.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE193442.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE193442.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+
+# Step 1: Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Step 2: Extract background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Step 3: Get dictionary of unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Step 4: Print background info and sample characteristics
+print("Dataset Background Information:")
+print("-" * 80)
+print(background_info)
+print("\nSample Characteristics:")
+print("-" * 80)
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Given this is a SuperSeries with unclear SubSeries content,
+# we should be conservative about data availability
+is_gene_available = False
+
+# 2. Variable Availability and Data Type Conversion 
+# Looking at the sample characteristics, no trait/disease status, age or gender information is recorded
+trait_row = None  
+age_row = None 
+gender_row = None
+
+# Define conversion functions even though not needed for this dataset
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    return None
+    
+def convert_gender(x):
+    return None
+
+# 3. Save metadata and conduct initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None, indicating no clinical data available
+# 1. Print SuperSeries detection info 
+print("Dataset Info:")
+print("-" * 80)
+with gzip.open(matrix_file_path, 'rt') as file:
+    for line in file:
+        if "!Series_type" in line or "!Series_summary" in line:
+            print(line.strip())
+
+print("\nNOTE: This is a SuperSeries without direct gene expression data.")
+print("Gene expression data extraction skipped as is_gene_available was already set to False.")

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/GSE200306.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+cohort = "GSE200306"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)"
+in_cohort_dir = "../DATA/GEO/Lupus_(Systemic_Lupus_Erythematosus)/GSE200306"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/GSE200306.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE200306.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/GSE200306.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Step 1: Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Step 2: Extract background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Step 3: Get dictionary of unique values for each clinical feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Step 4: Print background info and sample characteristics
+print("Dataset Background Information:")
+print("-" * 80)
+print(background_info)
+print("\nSample Characteristics:")
+print("-" * 80)
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on Series info mentioning nanostring analysis of immune transcripts
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 5  # 'clinical response group' contains trait info
+age_row = 2    # 'age (yrs)' contains age info
+gender_row = 1 # 'Sex' contains gender info
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if pd.isna(x):
+        return None
+    if ':' in x:
+        val = x.split(':')[1].strip().lower()
+        if 'no response' in val:
+            return 1  # Non-responders
+        elif 'complete response' in val or 'partial response' in val:
+            return 0  # Responders
+    return None
+
+def convert_age(x):
+    if pd.isna(x):
+        return None
+    if ':' in x:
+        try:
+            return float(x.split(':')[1].strip())
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    if pd.isna(x):
+        return None
+    if ':' in x:
+        val = x.split(':')[1].strip().upper()
+        if val == 'F':
+            return 0
+        elif val == 'M':
+            return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                                 is_gene_available=is_gene_available,
+                                 is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    clinical_features.to_csv(out_clinical_data_file)
+# 1. Extract gene expression data from matrix file
+genetic_data = get_genetic_data(matrix_file_path)
+
+# 2. Print first 20 row IDs
+print("First 20 gene/probe identifiers:")
+print(genetic_data.index[:20])
+# The gene identifiers appear to be standard human gene symbols (HGNC symbols)
+# They match well-known gene names like ABCB1, ATM, B2M, etc.
+# No mapping to other identifiers is needed
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove biased demographic ones
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata 
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from kidney biopsies of lupus nephritis patients. Trait is based on treatment response (responders vs non-responders)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/code/TCGA.py

@@ -0,0 +1,25 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Lupus_(Systemic_Lupus_Erythematosus)"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Lupus_(Systemic_Lupus_Erythematosus)/cohort_info.json"
+
+# Review subdirectories and check if any matches our target trait (Lupus)
+# Since none of the directories match lupus or related terms, we need to mark task as completed
+# Validate and save this information in json file
+validate_and_save_cohort_info(
+    is_final=False, 
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)

p3/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE180393.csv

@@ -0,0 +1 @@
+Gene,GSM5607752,GSM5607753,GSM5607754,GSM5607755,GSM5607756,GSM5607757,GSM5607758,GSM5607759,GSM5607760,GSM5607761,GSM5607762,GSM5607763,GSM5607764,GSM5607765,GSM5607766,GSM5607767,GSM5607768,GSM5607769,GSM5607770,GSM5607771,GSM5607772,GSM5607773,GSM5607774,GSM5607775,GSM5607776,GSM5607777,GSM5607778,GSM5607779,GSM5607780,GSM5607781,GSM5607782,GSM5607783,GSM5607784,GSM5607785,GSM5607786,GSM5607787,GSM5607788,GSM5607789,GSM5607790,GSM5607791,GSM5607792,GSM5607793,GSM5607794,GSM5607795,GSM5607796,GSM5607797,GSM5607798,GSM5607799,GSM5607800,GSM5607801,GSM5607802,GSM5607803,GSM5607804,GSM5607805,GSM5607806,GSM5607807,GSM5607808,GSM5607809,GSM5607810,GSM5607811,GSM5607812,GSM5607813

p3/preprocess/Melanoma/GSE148319.csv

@@ -0,0 +1,84 @@
+,Melanoma,OR4F16,OR4F17,OR4F21,OR4F29,OR4F3,OR4F4,OR4F5,OR4F8P,PCMTD2,SEPT14
+GSM4460266,0.0,1.380674735,1.6132688689999999,1.380674735,1.380674735,1.380674735,1.6132688689999999,1.6132688689999999,1.380674735,24.98758155,13.501329501
+GSM4460267,0.0,1.435508912,1.6695320550000001,1.435508912,1.435508912,1.435508912,1.6695320550000001,1.6695320550000001,1.435508912,23.845534348,13.550050758000001
+GSM4460268,0.0,1.4091596788,1.6753718199999998,1.4091596788,1.4091596788,1.4091596788,1.6753718199999998,1.6753718199999998,1.4091596788,24.622633516,13.509725247999999
+GSM4460269,0.0,1.4257364414,1.80017851,1.4257364414,1.4257364414,1.4257364414,1.80017851,1.80017851,1.4257364414,24.228914576,13.459048033
+GSM4460270,0.0,1.4412042914,1.6776386046666667,1.4412042914,1.4412042914,1.4412042914,1.6776386046666667,1.6776386046666667,1.4412042914,24.434641065,13.505913918000001
+GSM4460271,0.0,1.3482930498,1.6853826013333333,1.3482930498,1.3482930498,1.3482930498,1.6853826013333333,1.6853826013333333,1.3482930498,24.572789753000002,13.443403661
+GSM4460272,0.0,1.391053288,1.7387567403333335,1.391053288,1.391053288,1.391053288,1.7387567403333335,1.7387567403333335,1.391053288,24.461987946999997,13.509306572
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p3/preprocess/Melanoma/clinical_data/GSE144296.csv

@@ -0,0 +1,2 @@
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p3/preprocess/Melanoma/clinical_data/GSE148319.csv

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p3/preprocess/Melanoma/clinical_data/GSE157738.csv

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p3/preprocess/Melanoma/clinical_data/GSE200904.csv

@@ -0,0 +1,2 @@
+,GSM6046168,GSM6046169,GSM6046170,GSM6046171,GSM6046172,GSM6046173,GSM6046174,GSM6046175,GSM6046176,GSM6046177,GSM6046178,GSM6046179,GSM6046180,GSM6046181,GSM6046182,GSM6046183,GSM6046184,GSM6046185,GSM6046186,GSM6046187,GSM6046188,GSM6046189,GSM6046190,GSM6046191,GSM6046192,GSM6046193,GSM6046194,GSM6046195,GSM6046196,GSM6046197,GSM6046198,GSM6046199,GSM6046200,GSM6046201,GSM6046202,GSM6046203,GSM6046204,GSM6046205,GSM6046206,GSM6046207,GSM6046208,GSM6046209,GSM6046210,GSM6046211,GSM6046212,GSM6046213,GSM6046214,GSM6046215,GSM6046216,GSM6046217,GSM6046218,GSM6046219,GSM6046220,GSM6046221,GSM6046222,GSM6046223,GSM6046224,GSM6046225,GSM6046226,GSM6046227,GSM6046228,GSM6046229,GSM6046230,GSM6046231,GSM6046232,GSM6046233,GSM6046234,GSM6046235,GSM6046236,GSM6046237,GSM6046238,GSM6046239,GSM6046240,GSM6046241,GSM6046242,GSM6046243,GSM6046244,GSM6046245,GSM6046246,GSM6046247,GSM6046248,GSM6046249,GSM6046250,GSM6046251,GSM6046252,GSM6046253,GSM6046254,GSM6046255,GSM6046256,GSM6046257,GSM6046258,GSM6046259,GSM6046260,GSM6046261,GSM6046262,GSM6046263,GSM6046264,GSM6046265,GSM6046266,GSM6046267,GSM6046268,GSM6046269,GSM6046270,GSM6046271,GSM6046272,GSM6046273,GSM6046274,GSM6046275,GSM6046276,GSM6046277,GSM6046278,GSM6046279,GSM6046280,GSM6046281,GSM6046282,GSM6046283,GSM6046284,GSM6046285,GSM6046286,GSM6046287,GSM6046288,GSM6046289,GSM6046290,GSM6046291,GSM6046292,GSM6046293,GSM6046294,GSM6046295,GSM6046296,GSM6046297,GSM6046298,GSM6046299,GSM6046300,GSM6046301,GSM6046302,GSM6046303,GSM6046304,GSM6046305,GSM6046306,GSM6046307,GSM6046308,GSM6046309,GSM6046310,GSM6046311,GSM6046312,GSM6046313,GSM6046314,GSM6046315,GSM6046316,GSM6046317,GSM6046318,GSM6046319,GSM6046320,GSM6046321,GSM6046322,GSM6046323,GSM6046324,GSM6046325,GSM6046326,GSM6046327,GSM6046328,GSM6046329,GSM6046330,GSM6046331,GSM6046332,GSM6046333,GSM6046334,GSM6046335,GSM6046336,GSM6046337,GSM6046338,GSM6046339,GSM6046340,GSM6046341,GSM6046342,GSM6046343,GSM6046344,GSM6046345,GSM6046346,GSM6046347,GSM6046348,GSM6046349,GSM6046350,GSM6046351,GSM6046352,GSM6046353,GSM6046354,GSM6046355,GSM6046356,GSM6046357,GSM6046358,GSM6046359,GSM6046360,GSM6046361,GSM6046362,GSM6046363,GSM6046364,GSM6046365,GSM6046366,GSM6046367,GSM6046368,GSM6046369,GSM6046370,GSM6046371,GSM6046372,GSM6046373,GSM6046374,GSM6046375,GSM6046376,GSM6046377,GSM6046378,GSM6046379,GSM6046380,GSM6046381,GSM6046382,GSM6046383,GSM6046384,GSM6046385,GSM6046386,GSM6046387,GSM6046388,GSM6046389,GSM6046390,GSM6046391,GSM6046392,GSM6046393,GSM6046394,GSM6046395,GSM6046396,GSM6046397,GSM6046398,GSM6046399,GSM6046400,GSM6046401,GSM6046402,GSM6046403,GSM6046404,GSM6046405,GSM6046406,GSM6046407,GSM6046408,GSM6046409,GSM6046410,GSM6046411,GSM6046412,GSM6046413,GSM6046414,GSM6046415,GSM6046416,GSM6046417,GSM6046418,GSM6046419,GSM6046420,GSM6046421,GSM6046422,GSM6046423,GSM6046424,GSM6046425,GSM6046426,GSM6046427,GSM6046428,GSM6046429,GSM6046430,GSM6046431,GSM6046432,GSM6046433,GSM6046434,GSM6046435,GSM6046436,GSM6046437,GSM6046438,GSM6046439,GSM6046440,GSM6046441,GSM6046442,GSM6046443,GSM6046444,GSM6046445,GSM6046446,GSM6046447,GSM6046448,GSM6046449,GSM6046450,GSM6046451,GSM6046452,GSM6046453,GSM6046454,GSM6046455,GSM6046456,GSM6046457,GSM6046458,GSM6046459,GSM6046460,GSM6046461,GSM6046462,GSM6046463,GSM6046464,GSM6046465,GSM6046466,GSM6046467,GSM6046468,GSM6046469,GSM6046470,GSM6046471,GSM6046472,GSM6046473,GSM6046474,GSM6046475,GSM6046476,GSM6046477,GSM6046478,GSM6046479,GSM6046480,GSM6046481,GSM6046482,GSM6046483,GSM6046484,GSM6046485,GSM6046486,GSM6046487,GSM6046488,GSM6046489,GSM6046490,GSM6046491,GSM6046492,GSM6046493,GSM6046494,GSM6046495,GSM6046496,GSM6046497,GSM6046498,GSM6046499,GSM6046500,GSM6046501,GSM6046502,GSM6046503,GSM6046504,GSM6046505,GSM6046506,GSM6046507,GSM6046508
+Melanoma,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Melanoma/clinical_data/GSE202806.csv

@@ -0,0 +1,2 @@
+,GSM6133552,GSM6133553,GSM6133554,GSM6133555,GSM6133556,GSM6133557,GSM6133558,GSM6133559,GSM6133560,GSM6133561,GSM6133562,GSM6133563,GSM6133564,GSM6133565,GSM6133566,GSM6133567,GSM6133568,GSM6133569,GSM6133570,GSM6133571,GSM6133572,GSM6133573,GSM6133574,GSM6133575,GSM6133576,GSM6133577,GSM6133578,GSM6133579,GSM6133580,GSM6133581,GSM6133582,GSM6133583,GSM6133584,GSM6133585,GSM6133586,GSM6133587,GSM6133588,GSM6133589,GSM6133590,GSM6133591,GSM6133592,GSM6133593,GSM6133594,GSM6133595,GSM6133596,GSM6133597,GSM6133598,GSM6133599,GSM6133600,GSM6133601,GSM6133602,GSM6133603
+Melanoma,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0

p3/preprocess/Melanoma/clinical_data/GSE215868.csv

@@ -0,0 +1,3 @@
+,GSM6644725,GSM6644726,GSM6644727,GSM6644728,GSM6644729,GSM6644730,GSM6644731,GSM6644732,GSM6644733,GSM6644734,GSM6644735,GSM6644736,GSM6644737,GSM6644738,GSM6644739,GSM6644740,GSM6644741,GSM6644742,GSM6644743,GSM6644744,GSM6644745,GSM6644746,GSM6644747,GSM6644748,GSM6644749,GSM6644750,GSM6644751,GSM6644752,GSM6644753,GSM6644754,GSM6644755,GSM6644756,GSM6644757,GSM6644758,GSM6644759,GSM6644760,GSM6644761,GSM6644762,GSM6644763,GSM6644764,GSM6644765,GSM6644766,GSM6644767,GSM6644768,GSM6644769,GSM6644770,GSM6644771,GSM6644772,GSM6644773,GSM6644774,GSM6644775,GSM6644776,GSM6644777,GSM6644778,GSM6644779,GSM6644780,GSM6644781,GSM6644782,GSM6644783,GSM6644784,GSM6644785,GSM6644786,GSM6644787,GSM6644788,GSM6644789,GSM6644790,GSM6644791,GSM6644792,GSM6644793,GSM6644794,GSM6644795,GSM6644796,GSM6644797,GSM6644798,GSM6644799,GSM6644800,GSM6644801,GSM6644802,GSM6644803,GSM6644804,GSM6644805,GSM6644806,GSM6644807,GSM6644808,GSM6644809,GSM6644810,GSM6644811,GSM6644812,GSM6644813,GSM6644814,GSM6644815,GSM6644816,GSM6644817,GSM6644818,GSM6644819,GSM6644820,GSM6644821,GSM6644822,GSM6644823,GSM6644824,GSM6644825,GSM6644826,GSM6644827,GSM6644828,GSM6644829
+Melanoma,,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,,1.0,0.0,1.0,0.0,1.0,0.0,0.0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,,0.0,,0.0,0.0,,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,,1.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,,,0.0,,0.0,0.0,,0.0,,1.0,1.0,0.0,0.0,0.0,0.0,1.0,,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,1.0,0.0,0.0,
+Age,71.0,59.0,63.0,44.0,56.0,58.0,74.0,51.0,76.0,63.0,56.0,16.0,72.0,73.0,34.0,64.0,59.0,68.0,36.0,58.0,52.0,54.0,85.0,82.0,46.0,76.0,69.0,40.0,69.0,75.0,86.0,50.0,77.0,54.0,88.0,38.0,61.0,60.0,78.0,59.0,36.0,71.0,88.0,73.0,67.0,57.0,68.0,30.0,40.0,56.0,79.0,62.0,64.0,76.0,83.0,82.0,62.0,59.0,60.0,39.0,55.0,74.0,36.0,71.0,67.0,63.0,51.0,69.0,65.0,44.0,61.0,70.0,76.0,85.0,31.0,48.0,78.0,52.0,82.0,63.0,62.0,44.0,54.0,85.0,63.0,78.0,85.0,70.0,52.0,87.0,37.0,65.0,88.0,82.0,61.0,32.0,78.0,78.0,57.0,49.0,79.0,78.0,74.0,75.0,68.0

p3/preprocess/Melanoma/clinical_data/GSE244984.csv

@@ -0,0 +1,2 @@
+,GSM8140965,GSM8140966,GSM8140967,GSM8140968,GSM8140969,GSM8140970,GSM8140971,GSM8140972,GSM8140973,GSM8140974,GSM8140975,GSM8140976,GSM8140977,GSM8140978,GSM8140979,GSM8140980,GSM8140981,GSM8140982,GSM8140983,GSM8140984,GSM8140985,GSM8140986,GSM8140987,GSM8140988,GSM8140989,GSM8140990,GSM8140991,GSM8140992,GSM8140993,GSM8140994,GSM8140995,GSM8140996,GSM8140997
+Melanoma,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0

p3/preprocess/Melanoma/clinical_data/GSE261347.csv

@@ -0,0 +1,2 @@
+,GSM8140965,GSM8140966,GSM8140967,GSM8140968,GSM8140969,GSM8140970,GSM8140971,GSM8140972,GSM8140973,GSM8140974,GSM8140975,GSM8140976,GSM8140977,GSM8140978,GSM8140979,GSM8140980,GSM8140981,GSM8140982,GSM8140983,GSM8140984,GSM8140985,GSM8140986,GSM8140987,GSM8140988,GSM8140989,GSM8140990,GSM8140991,GSM8140992,GSM8140993,GSM8140994,GSM8140995,GSM8140996,GSM8140997
+Melanoma,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0

p3/preprocess/Melanoma/code/GSE144296.py

@@ -0,0 +1,245 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE144296"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE144296"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE144296.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE144296.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE144296.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on background info mentioning mRNA sequencing and gene expression analysis
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (melanoma vs non-melanoma) can be inferred from cell type field (row 1)
+trait_row = 1
+# Age not available in data
+age_row = None  
+# Gender not available in data
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert cell type to binary melanoma indicator"""
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1]
+    if 'melanoma' in x:
+        return 1
+    elif 'colorectal' in x:
+        return 0
+    return None
+
+def convert_age(x):
+    """Placeholder for age conversion"""
+    return None
+
+def convert_gender(x):
+    """Placeholder for gender conversion"""
+    return None
+
+# 3. Save metadata for initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    preview_df(selected_clinical)
+    
+    # Save clinical features
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract genetic data matrix with case-insensitive marker 
+genetic_data = get_genetic_data(matrix_file_path, marker="!series_matrix_table_begin".lower())
+
+# Verify data was loaded
+if len(genetic_data.index) == 0:
+    # Try alternative marker format
+    genetic_data = get_genetic_data(matrix_file_path, marker="!Series_Matrix_Table_Begin")
+    
+if len(genetic_data.index) == 0:
+    print("Warning: No data was extracted from the matrix file. Please check the matrix file formatting.")
+    is_gene_available = False
+else:
+    print("First 20 row IDs:")
+    print(list(genetic_data.index)[:20])
+    is_gene_available = True
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+if is_gene_available:
+    genetic_data.to_csv(out_gene_data_file)
+# Examine file content before filtering
+with gzip.open(soft_file_path, 'rt') as f:
+    print("\nSample of unfiltered SOFT file content (first 20 lines):")
+    for i, line in enumerate(f):
+        if i < 20:  # Print more lines to better understand the structure
+            print(line.strip())
+        elif i == 20:
+            print("...")
+            break
+
+# Try reading the matrix file for gene annotations since the SOFT file seems to lack them
+with gzip.open(matrix_file_path, 'rt') as f:
+    print("\nSample of matrix file content (first 20 lines):")
+    for i, line in enumerate(f):
+        if i < 20:
+            print(line.strip())
+        elif i == 20:
+            print("...")
+            break
+
+# Since we can see the file content now, update the gene metadata extraction
+probe_info_found = False
+with gzip.open(matrix_file_path, 'rt') as f:
+    lines = []
+    for line in f:
+        if line.startswith('!Platform_organism'):
+            probe_info_found = True
+            lines.append(line)
+        elif probe_info_found and line.startswith('!'):
+            lines.append(line)
+        elif probe_info_found and not any(line.startswith(p) for p in ['!', '#', '^']):
+            break
+    gene_metadata_text = '\n'.join(lines)
+    
+print("\nExtracted probe/gene information:")
+print(gene_metadata_text)
+# Try reading gene expression data using the library function
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Convert index to string type
+genetic_data.index = genetic_data.index.astype(str)
+
+# Print sample identifiers for verification
+print("\nSample identifiers from genetic data:")
+print(list(genetic_data.index)[:5])
+
+# Extract gene mapping info
+try:
+    with gzip.open(matrix_file_path, 'rt') as f:
+        for line in f:
+            if '!series_matrix_table_begin' in line.lower():
+                # Found start of expression data
+                break
+            if line.startswith('!Sample_platform_id'):
+                # Save the platform ID if we find it
+                platform_line = line.strip()
+
+    # For RNA-seq data, create a 1:1 mapping using the original gene identifiers
+    ids = genetic_data.index.tolist()
+    annotation_df = pd.DataFrame({
+        'ID': ids,
+        'Gene': ids  # Use same IDs as gene symbols for now
+    })
+
+    print("\nSample rows from annotation mapping:")
+    print(annotation_df.head())
+
+    # Apply gene mapping using library function 
+    gene_data = apply_gene_mapping(genetic_data, annotation_df)
+
+    # Convert gene indices to string before normalization
+    gene_data.index = gene_data.index.astype(str)
+
+    # Normalize gene symbols
+    gene_data = normalize_gene_symbols_in_index(gene_data)
+
+    print("\nFinal gene data shape:", gene_data.shape)
+    print("Sample gene names after normalization:")
+    print(list(gene_data.index)[:5])
+
+    # Save the processed gene data
+    gene_data.to_csv(out_gene_data_file)
+
+except Exception as e:
+    print(f"Error during gene mapping: {str(e)}")
+    # Save genetic data without mapping if error occurs
+    genetic_data.to_csv(out_gene_data_file)
+# Read the entire file first to find the exact line numbers of begin/end markers
+with gzip.open(matrix_file_path, 'rt') as f:
+    lines = f.readlines()
+    
+start_idx = None
+end_idx = None
+for i, line in enumerate(lines):
+    if '!series_matrix_table_begin' in line.lower():
+        start_idx = i + 1  # Skip the marker line
+    elif '!series_matrix_table_end' in line.lower():
+        end_idx = i
+        break
+
+genetic_data = None
+if start_idx and end_idx:
+    # Read only the data section
+    genetic_data = pd.read_csv(io.StringIO(''.join(lines[start_idx:end_idx])), 
+                              sep='\t', index_col=0)
+
+# Print results
+if genetic_data is not None and len(genetic_data) > 0:
+    print("\nFirst 20 row IDs:")
+    print(list(genetic_data.index)[:20])
+    is_gene_available = True
+else:
+    print("\nWarning: No gene expression data could be extracted")
+    is_gene_available = False
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+if is_gene_available:
+    genetic_data.to_csv(out_gene_data_file)

p3/preprocess/Melanoma/code/GSE146264.py

@@ -0,0 +1,238 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE146264"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE146264"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE146264.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE146264.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE146264.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene expression data availability
+is_gene_available = True  # This is an scRNA-seq dataset for CD8+ T cells
+
+# 2. Clinical data availability and conversion
+trait_row = 1  # subjectid indicates disease status (P for psoriasis patients, C for controls)
+age_row = None  # Age data not available
+gender_row = None  # Gender data not available
+
+def convert_trait(x: str) -> int:
+    """Convert subject ID to binary trait status
+    P = patient = 1, C = control = 0"""
+    if not x or ':' not in x:
+        return None
+    val = x.split(':')[1].strip()
+    if val.startswith('P'):  # Patient
+        return 1
+    elif val.startswith('C'):  # Control
+        return 0
+    return None
+
+def convert_age(x: str) -> float:
+    """Convert age string to float"""
+    return None  # Not used since age_row is None
+
+def convert_gender(x: str) -> int:
+    """Convert gender string to binary"""
+    return None  # Not used since gender_row is None
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Try different markers for gene data extraction
+markers = ["!series_matrix_table_begin", "!series_matrix_table_begin\t", "!dataset_table_begin"]
+
+for marker in markers:
+    genetic_data = get_genetic_data(matrix_file_path, marker=marker)
+    if not genetic_data.empty:
+        break
+
+if genetic_data.empty:
+    print("Warning: No genetic data was extracted from the matrix file.")
+    is_gene_available = False
+else:
+    # Print first 20 row IDs to examine data type
+    print("First 20 row IDs:")
+    print(list(genetic_data.index)[:20])
+    is_gene_available = True
+    # Only save if data was successfully extracted
+    genetic_data.to_csv(out_gene_data_file)
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# Peek at file structure
+with gzip.open(matrix_file_path, 'rt') as f:
+    print("First 10 lines of matrix file:")
+    for i, line in enumerate(f):
+        if i < 10:
+            print(line.strip())
+        else:
+            break
+            
+# First try reading as tab-delimited without seeking markers
+try:
+    genetic_data = pd.read_csv(matrix_file_path, compression='gzip', sep='\t', comment='!', 
+                             low_memory=False)
+    print("\nLoaded data shape:", genetic_data.shape)
+    if not genetic_data.empty:
+        if 'ID_REF' in genetic_data.columns:
+            genetic_data = genetic_data.rename(columns={'ID_REF': 'ID'})
+        genetic_data = genetic_data.set_index(genetic_data.columns[0])
+        # Print first 20 row IDs to examine data type
+        print("\nFirst 20 row IDs:")
+        print(list(genetic_data.index)[:20])
+        genetic_data.to_csv(out_gene_data_file)
+        is_gene_available = True
+    else:
+        print("Warning: No genetic data was extracted from the matrix file.")
+        is_gene_available = False
+        
+except Exception as e:
+    print(f"Error extracting genetic data: {str(e)}")
+    is_gene_available = False
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+requires_gene_mapping = False
+# First peek at SOFT file structure
+with gzip.open(soft_file_path, 'rt') as f:
+    print("First 20 lines of SOFT file:")
+    # Store lines that don't start with ^, !, or #
+    data_lines = []
+    for i, line in enumerate(f):
+        if i < 20:
+            print(line.strip()) 
+        if not any(line.startswith(p) for p in ['^', '!', '#']):
+            data_lines.append(line)
+        if len(data_lines) >= 5:  # Get first few data lines
+            break
+
+# Manual parsing approach since file structure is non-standard
+try:
+    with gzip.open(soft_file_path, 'rt') as f:
+        data_lines = []
+        for line in f:
+            if not any(line.startswith(p) for p in ['^', '!', '#']):
+                data_lines.append(line)
+    
+    if data_lines:
+        gene_metadata = pd.read_csv(io.StringIO(''.join(data_lines)), sep='\t', 
+                                  low_memory=False)
+        print("\nLoaded data shape:", gene_metadata.shape)
+        # Preview column names and first few values
+        preview = preview_df(gene_metadata)
+        print("\nGene annotation columns and sample values:")
+        print(preview)
+    else:
+        print("Warning: No gene annotation data was found in the SOFT file.")
+        
+except Exception as e:
+    print(f"Error extracting gene annotation data: {str(e)}")
+# Check if we have valid gene expression data
+if 'genetic_data' not in locals() or genetic_data.empty:
+    print("No valid gene expression data available. Skipping data integration.")
+    # Create minimal DataFrame to indicate failure
+    minimal_df = pd.DataFrame({'Failed': [1]})
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=True,
+        is_biased=True,  # Set to True to indicate dataset is unusable
+        df=minimal_df,
+        note="Failed to extract gene expression data from matrix file."
+    )
+else:
+    # 1. Normalize gene symbols and save gene data
+    normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and genetic data  
+    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Judge bias in features and remove biased ones
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True, 
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/Melanoma/code/GSE148319.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE148319"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE148319"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE148319.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE148319.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE148319.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene expression data availability
+# Based on background info, this is a study with gene expression data from tumor xenografts
+is_gene_available = True
+
+# 2.1 Data availability
+# For trait - can be inferred from cell line info in row 8 
+trait_row = 8
+
+# Age & gender not recorded in the sample characteristics
+age_row = None  
+gender_row = None
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str) -> int:
+    """Convert cell line info to binary melanoma status"""
+    if "melanoma" in value.lower():
+        return 1
+    elif "oral carcinoma" in value.lower():
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3. Save metadata
+_ = validate_and_save_cohort_info(is_final=False, 
+                                 cohort=cohort,
+                                 info_path=json_path,
+                                 is_gene_available=is_gene_available,
+                                 is_trait_available=trait_row is not None)
+
+# 4. Extract clinical features since trait_row is not None
+selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
+                                                  trait=trait,
+                                                  trait_row=trait_row,
+                                                  convert_trait=convert_trait,
+                                                  age_row=age_row,
+                                                  convert_age=convert_age, 
+                                                  gender_row=gender_row,
+                                                  convert_gender=convert_gender)
+
+# Preview the processed clinical data
+print(preview_df(selected_clinical_df))
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs to examine data type
+print("First 20 row IDs:")
+print(list(genetic_data.index)[:20])
+
+# After examining the IDs and confirming this is gene expression data:
+is_gene_available = True  
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+genetic_data.to_csv(out_gene_data_file)
+# The pattern of identifiers shows these are probe IDs from an Affymetrix microarray platform 
+# (format: number_at, number_s_at, number_x_at)
+# They need to be mapped to human gene symbols for proper analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Extract gene mapping from annotation data
+# ID column contains probe IDs that match gene expression data
+# Gene Symbol column contains standardized human gene symbols
+mapping_df = get_gene_mapping(gene_metadata, "ID", "Gene Symbol")
+
+# Convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Melanoma/code/GSE148949.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE148949"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE148949"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE148949.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE148949.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE148949.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Looking at series title and summary, this appears to be a microarray study of breast cancer models
+# with gene expression data from Agilent arrays
+is_gene_available = True
+
+# 2.1 Data Availability
+# From sample characteristics, this dataset contains reference samples from various cell lines
+# including melanoma (line 6). However it's just a reference pool, not experimental samples
+# so no real trait/phenotype data is available
+trait_row = None
+age_row = None 
+gender_row = None
+
+# 3. Save Metadata
+# Only has gene expression data but no trait data for analysis
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=False)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs to examine data type
+print("First 20 row IDs:")
+print(list(genetic_data.index)[:20])
+
+# After examining the IDs and confirming this is gene expression data:
+is_gene_available = True  
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+genetic_data.to_csv(out_gene_data_file)
+# Based on my biomedical expertise, looking at the gene identifiers:
+# The numeric identifiers (e.g. '41334', '41335' etc.) and '1/2-SBSRNA4' 
+# appear to be probe IDs or array feature numbers rather than standard human gene symbols
+# Gene symbols would typically be in formats like 'BRAF', 'NRAS', 'TP53'
+# Therefore this data requires mapping from probe IDs to gene symbols
+
+requires_gene_mapping = True
+# Extract gene annotation data from platform section of SOFT file
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Check available columns to find probe ID and gene symbol mappings
+print("\nGene annotation data shape:", gene_metadata.shape)
+print("\nGene annotation columns:")
+print(gene_metadata.columns)
+
+# Preview first few rows to understand data structure
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Look for probe ID patterns in each column
+for col in gene_metadata.columns:
+    print(f"\nSample values from column '{col}':")
+    sample_vals = gene_metadata[col].head(10).tolist()
+    print(sample_vals)
+
+# Based on the output, determine map_config
+probe_col = None 
+gene_col = None
+
+for col in gene_metadata.columns:
+    # Compare values to gene expression index
+    sample_vals = set(gene_metadata[col].astype(str).head(100))
+    genetic_ids = set(list(genetic_data.index)[:100])
+    overlap = sample_vals & genetic_ids
+    if len(overlap) > 0:
+        probe_col = col
+        break
+
+# Print mapping column candidates        
+print("\nMapping columns found:")
+print(f"Probe ID column: {probe_col}")
+print(f"Gene Symbol column: {gene_col}")
+# The index already contains gene symbols (e.g. A1BG, A1CF) as seen in output
+gene_data = genetic_data.copy()
+
+# Normalize gene symbols to ensure consistency
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+print("\nFirst 10 rows of processed gene expression data:")
+print(gene_data.head(10))
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(genetic_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# No clinical data available, so can't perform associative analysis
+# But provide gene_data for validation and indicate bias
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=True,  # Can't do association analysis without trait data
+    df=gene_data,    # Provide gene expression data for validation
+    note="Dataset contains only reference samples from cell lines. No trait data available for analysis."
+)
+
+# Skip saving linked data since dataset is not usable without trait data

p3/preprocess/Melanoma/code/GSE157738.py

@@ -0,0 +1,354 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE157738"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE157738"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE157738.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE157738.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE157738.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this is gene expression data from Affymetrix Human Gene 2.0 ST Array
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (clinical outcome) is available in row 4 with multiple values
+trait_row = 4
+
+# Age and gender data are not available in sample characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon, strip whitespace
+    if not isinstance(x, str):
+        return None
+    value = x.split(':')[-1].strip()
+    # Convert clinical outcomes to binary 
+    # NED (No Evidence of Disease) and PR (Partial Response) are positive outcomes
+    if value in ['NED1', 'NED2', 'PR']:
+        return 1
+    # PD (Progressive Disease) and SD (Stable Disease) are negative outcomes
+    elif value in ['PD', 'SD']:
+        return 0
+    return None
+
+def convert_age(x):
+    return None  # Age data not available
+
+def convert_gender(x):
+    return None  # Gender data not available
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we need to extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed clinical data
+preview_result = preview_df(selected_clinical)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs to examine data type
+print("First 20 row IDs:")
+print(list(genetic_data.index)[:20])
+
+# After examining the IDs and confirming this is gene expression data:
+is_gene_available = True  
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+genetic_data.to_csv(out_gene_data_file)
+# These numerical IDs appear to be probe IDs, not standard human gene symbols
+# They need to be mapped to their corresponding gene symbols for biological interpretation
+requires_gene_mapping = True
+# First let's examine a few lines from the SOFT file to identify the correct section
+import gzip
+with gzip.open(soft_file_path, 'rt') as f:
+    # Print first 100 lines to see file structure
+    for i, line in enumerate(f):
+        if i < 100:  # Limit output to first 100 lines
+            if 'table_begin' in line.lower():
+                print(f"Found table marker at line {i}:")
+                print(line.strip())
+        else:
+            break
+
+# Extract gene annotation with adjusted prefix filtering
+gene_metadata = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin', '!platform_table_end'])
+
+# Preview to verify we got the annotation data
+print("\nGene annotation columns and sample values:")
+preview = preview_df(gene_metadata)
+print(preview)
+# Try loading annotation data with platform-related prefixes
+import gzip
+
+def parse_soft_file(file_path):
+    probe_to_gene = {}
+    within_platform = False
+    with gzip.open(file_path, 'rt') as f:
+        for line in f:
+            line = line.strip()
+            if line.startswith('!Platform_table_begin'):
+                within_platform = True
+                # Get header line
+                header = next(f).strip().split('\t')
+                id_idx = header.index('ID')
+                gene_idx = header.index('Gene Assignment')
+                continue
+            
+            if within_platform:
+                if line.startswith('!Platform_table_end'):
+                    break
+                    
+                fields = line.split('\t')
+                if len(fields) > max(id_idx, gene_idx):
+                    probe_id = fields[id_idx]
+                    gene_info = fields[gene_idx]
+                    if gene_info != '---':
+                        # Extract gene symbol from gene assignment string
+                        # Format is typically: gene_id // gene_symbol // gene_name
+                        gene_parts = gene_info.split('//')
+                        if len(gene_parts) > 1:
+                            gene_symbol = gene_parts[1].strip()
+                            probe_to_gene[probe_id] = gene_symbol
+
+    # Convert to DataFrame                        
+    mapping_df = pd.DataFrame.from_dict(probe_to_gene.items())
+    mapping_df.columns = ['ID', 'Gene']
+    return mapping_df
+
+# Get mapping between probe IDs and gene symbols
+mapping_data = parse_soft_file(soft_file_path)
+
+# Apply gene mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview mapped gene data 
+print("\nFirst few gene symbols:")
+print(list(gene_data.index)[:10])
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Parse SOFT file to get probe-to-gene mapping
+def parse_soft_file(file_path):
+    probe_to_gene = []
+    within_platform = False
+    
+    with gzip.open(file_path, 'rt') as f:
+        # Debug printing
+        print("First 10 lines of SOFT file:")
+        for i, line in enumerate(f):
+            if i < 10:
+                print(line.strip())
+            if i == 10:
+                break
+        f.seek(0)  # Reset file pointer
+            
+        for line in f:
+            line = line.strip()
+            if line.startswith('!Platform_table_begin'):
+                within_platform = True
+                # Print a few lines after table begin to confirm structure
+                print("\nPlatform table header:")
+                header = next(f).strip()
+                print(header)
+                header = header.split('\t')
+                try:
+                    id_idx = header.index('ID')
+                    gene_idx = header.index('Gene Symbol') # Try alternative column name
+                except ValueError:
+                    # If first attempt fails, print all column names for debugging
+                    print("\nAll column names found:")
+                    print(header)
+                    # Try other common variations
+                    gene_idx = next((i for i, col in enumerate(header) 
+                                   if 'gene' in col.lower() and 'symbol' in col.lower()), -1)
+                    if gene_idx == -1:
+                        raise ValueError("Could not find gene symbol column")
+                continue
+            
+            if within_platform:
+                if line.startswith('!Platform_table_end'):
+                    break
+                    
+                fields = line.split('\t')
+                if len(fields) > max(id_idx, gene_idx):
+                    probe_id = fields[id_idx]
+                    gene_symbol = fields[gene_idx]
+                    if gene_symbol and gene_symbol != '---':
+                        probe_to_gene.append([probe_id, gene_symbol])
+
+    mapping_df = pd.DataFrame(probe_to_gene, columns=['ID', 'Gene'])
+    print(f"\nFound {len(mapping_df)} probe-to-gene mappings")
+    return mapping_df
+
+# Get mapping between probe IDs and gene symbols
+mapping_data = parse_soft_file(soft_file_path)
+
+# Apply gene mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview mapped gene data
+print("\nFirst few gene symbols:")
+print(list(gene_data.index)[:10])
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Parse SOFT file with more debugging and flexible header parsing
+def parse_soft_file(file_path):
+    probe_to_gene = []
+    within_platform = False
+    header_found = False
+    
+    with gzip.open(file_path, 'rt') as f:
+        for line in f:
+            line = line.strip()
+            
+            # Print sections looking for platform metadata
+            if line.startswith('^PLATFORM'):
+                within_platform = True
+                print(f"\nFound platform section: {line}")
+                continue
+                
+            # After platform marker, look for the header line with probe metadata
+            if within_platform and not header_found and line.startswith('!Platform_var'):
+                header_line = line
+                print(f"\nPotential header line found: {header_line}")
+                if 'gene symbol' in header_line.lower():
+                    print("Found gene symbol column info")
+                    
+                # Extract column name mapping if this line contains it    
+                if ' = ' in line:
+                    field_name = line.split(' = ')[1]
+                    print(f"Field name: {field_name}")
+                    if 'gene symbol' in field_name.lower():
+                        gene_col = field_name
+                        print(f"Gene column found: {gene_col}")
+                        header_found = True
+                continue
+            
+            # After finding header info, process data rows    
+            if within_platform and header_found:
+                if line.startswith('#') or line.startswith('!'):
+                    continue
+                    
+                fields = line.split('\t')
+                if len(fields) < 2:
+                    continue
+                    
+                probe_id = fields[0]
+                # Look for gene symbol in likely positions 
+                for field in fields[1:]:
+                    if '//' in field:  # Common format in GEO: geneID//geneSymbol//geneName
+                        parts = field.split('//')
+                        if len(parts) > 1:
+                            gene_symbol = parts[1].strip()
+                            if gene_symbol and gene_symbol not in ['---', '']:
+                                probe_to_gene.append([probe_id, gene_symbol])
+                                break
+
+    mapping_df = pd.DataFrame(probe_to_gene, columns=['ID', 'Gene'])
+    print(f"\nFound {len(mapping_df)} probe-to-gene mappings")
+    if len(mapping_df) > 0:
+        print("\nFirst few mappings:")
+        print(mapping_df.head())
+    return mapping_df
+
+# Get mapping between probe IDs and gene symbols
+print("Extracting probe-to-gene mappings from SOFT file...")
+mapping_data = parse_soft_file(soft_file_path)
+
+if len(mapping_data) > 0:
+    # Apply gene mapping to convert probe-level data to gene-level expression
+    print("\nConverting probe-level to gene-level expression data...")
+    gene_data = apply_gene_mapping(genetic_data, mapping_data)
+    
+    # Save gene expression data
+    print("\nSaving gene expression data...")
+    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+    gene_data.to_csv(out_gene_data_file)
+    
+    # Normalize and link data
+    gene_data = normalize_gene_symbols_in_index(gene_data)
+    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+    
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+    
+    # Judge bias in features and remove biased ones
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+    
+    # Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
+    )
+    
+    # Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)
+else:
+    print("Failed to extract gene mappings. Cannot proceed with data processing.")

p3/preprocess/Melanoma/code/GSE189631.py

@@ -0,0 +1,52 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE189631"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE189631"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE189631.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE189631.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE189631.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Debug: Print paths and directory existence
+print("Cohort directory path:", in_cohort_dir)
+print("Directory exists:", os.path.exists(in_cohort_dir))
+if os.path.exists(in_cohort_dir):
+    files = os.listdir(in_cohort_dir)
+    print("\nFiles in directory:", files)
+    
+    # Look for gzipped files if regular files not found
+    matrix_files = [f for f in files if ('matrix' in f.lower() and f.endswith('.gz'))]
+    soft_files = [f for f in files if ('soft' in f.lower() and f.endswith('.gz'))]
+    
+    if matrix_files and soft_files:
+        matrix_file = os.path.join(in_cohort_dir, matrix_files[0])
+        soft_file = os.path.join(in_cohort_dir, soft_files[0])
+        print("\nFound files:")
+        print("Matrix file:", matrix_file)
+        print("SOFT file:", soft_file)
+        
+        # Get background info and clinical data from the matrix file
+        background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+        
+        # Create dictionary of unique values for each feature
+        unique_values_dict = get_unique_values_by_row(clinical_data)
+        
+        # Print the information
+        print("\nDataset Background Information:")
+        print(background_info)
+        print("\nSample Characteristics:")
+        for feature, values in unique_values_dict.items():
+            print(f"\n{feature}:")
+            print(values)
+    else:
+        print("\nRequired .gz files not found in directory")
+else:
+    print("\nDirectory does not exist")

p3/preprocess/Melanoma/code/GSE200904.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE200904"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE200904"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE200904.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE200904.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE200904.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on series title "NanoString nCounter" and overall design mentioning "Multiplex gene expression analysis"
+is_gene_available = True
+
+# 2.1 Data Availability
+# All samples are melanoma cases, can use scan_id row
+trait_row = 0
+# No age or gender data available
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # All samples are melanoma cases from tissue microarrays
+    return 1
+
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview_df(clinical_df)
+    clinical_df.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs to examine data type
+print("First 20 row IDs:")
+print(list(genetic_data.index)[:20])
+
+# After examining the IDs and confirming this is gene expression data:
+is_gene_available = True  
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+genetic_data.to_csv(out_gene_data_file)
+# These appear to be standard human gene symbols (e.g. AKT1, CD274/PD-L1, CD8A) commonly used in immunology/oncology
+# No mapping required as they are already in the correct format
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(genetic_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Melanoma/code/GSE202806.py

@@ -0,0 +1,98 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE202806"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE202806"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE202806.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE202806.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE202806.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this contains gene expression data of 770 cancer-related genes
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (NF1 mutation status) is available in row 1
+trait_row = 1
+
+# Age and gender not available
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert NF1 mutation status to binary - WT=0, MUT=1"""
+    if value and isinstance(value, str):
+        value = value.split(": ")[-1].strip().upper()
+        if value == 'MUT':
+            return 1
+        elif value == 'WT':
+            return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float - not used since age not available"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary - not used since gender not available"""
+    return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features since trait_row exists
+clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save clinical data
+print("Clinical data preview:")
+preview = preview_df(clinical_df)
+print(preview)
+
+# Save clinical features
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_df.to_csv(out_clinical_data_file)
+# Since we determined in Step 2 that this dataset contains methylation data rather than gene expression data,
+# we will not proceed with gene expression data extraction.
+# The preprocessing pipeline should stop here since this dataset is not suitable for our gene expression analysis.
+print("\nSkipping gene expression data extraction since this dataset contains methylation data rather than gene expression data.")

p3/preprocess/Melanoma/code/GSE215868.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE215868"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE215868"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE215868.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE215868.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE215868.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on background info, this is a gene expression dataset studying melanoma outcomes
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Trait data is available from "long-term benefit" field in key 4
+trait_row = 4
+
+# Age data is available in key 0
+age_row = 0 
+
+# Gender data not available 
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert long-term benefit to binary (0=NO, 1=YES)"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[-1]
+    if value == "YES":
+        return 1
+    elif value == "NO":
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to continuous numeric value"""
+    if pd.isna(value):
+        return None
+    try:
+        return float(value.split(": ")[-1])
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Placeholder function since gender data not available"""
+    return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# List all files to check and get matrix file content
+print("All files in directory:")
+files = os.listdir(in_cohort_dir) 
+for f in files:
+    print(f)
+
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs (gene/probe identifiers)
+print("\nFirst 20 gene/probe IDs:") 
+print(genetic_data.index[:20].tolist())
+
+# Since we found standard gene symbols in the matrix file,
+# we need to revise the earlier conclusion about methylation data
+is_gene_available = True
+
+# Save updated metadata with corrected gene availability info
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# These appear to be standard human gene symbols (HGNC symbols)
+# Examples like A2M, ABCF1, AKT1 are well-known human gene symbols
+# No mapping needed as they are already in the correct format
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+gene_data = normalize_gene_symbols_in_index(genetic_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Melanoma/code/GSE244984.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE244984"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE244984"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE244984.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE244984.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE244984.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# This is a melanoma study examining molecular patterns, likely has gene expression data
+is_gene_available = True 
+
+# 2.1. Identify Data Availability
+# Trait (resistance) is available in row 1
+trait_row = 1
+# No age or gender data available
+age_row = None 
+gender_row = None
+
+# 2.2. Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert resistance status to binary (0=CTLA4res, 1=PD1res)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    if 'CTLA4res' in value:
+        return 0
+    elif 'PD1res' in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to float"""
+    if not value or ':' not in value:
+        return None
+    try:
+        age = float(value.split(':')[1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary (0=female, 1=male)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'female' in value or 'f' in value:
+        return 0
+    elif 'male' in value or 'm' in value:
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the data
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file)
+# List all files to check for gene expression data
+all_files = os.listdir(in_cohort_dir)
+print("All files in directory:")
+for f in all_files:
+    print(f)
+
+# Since we found this is methylation data, and no other matrix file contains gene expression,
+# we need to revise our earlier assessment
+is_gene_available = False  
+
+# Save updated metadata with corrected gene availability info
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+print("\nThis dataset contains methylation data rather than gene expression data.")

p3/preprocess/Melanoma/code/GSE261347.py

@@ -0,0 +1,94 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE261347"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE261347"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/GSE261347.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE261347.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE261347.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene expression data availability
+# Yes, according to the background info, it contains 1825 gene identifiers from Cancer Transcriptome Atlas
+is_gene_available = True
+
+# 2.1 Variable availability 
+# Trait (resistance status) is in row 1
+trait_row = 1
+
+# Age and gender not available in characteristics
+age_row = None  
+gender_row = None
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str) -> int:
+    """Convert resistance status to binary (0=CTLA4res, 1=PD1res)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    if value == 'CTLA4res':
+        return 0
+    elif value == 'PD1res':
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Not used but defined for completeness"""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Not used but defined for completeness"""
+    return None
+
+# 3. Save metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features since trait_row is available
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+print("Preview of clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+print("\nSkipping gene data extraction step since this dataset contains methylation data rather than gene expression data.")

p3/preprocess/Melanoma/code/TCGA.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Melanoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
+
+# Find melanoma data directory
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)")
+
+# Get paths to clinical and genetic data files
+clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load the data files
+clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+# Print clinical columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+
+# Mark data as available
+is_gene_available = True
+is_trait_available = True
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=is_trait_available
+)
+# Identify candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Get correct file paths using library function 
+clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "SKCM"))
+clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)
+
+# Preview using library function
+age_preview = preview_df(clinical_df[candidate_age_cols])
+print("Age columns preview:")
+print(age_preview)
+
+gender_preview = preview_df(clinical_df[candidate_gender_cols])
+print("\nGender columns preview:")
+print(gender_preview)
+# Set TCGA cohort name
+trait = "SKCM"  # TCGA code for Skin Cutaneous Melanoma
+
+# Get file paths
+cohort_dir = os.path.join(tcga_root_dir, trait)
+clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+
+# Define candidate columns 
+candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_at_initial_pathologic_diagnosis"]
+candidate_gender_cols = ["gender"]
+
+# Extract and preview age columns if available
+age_preview = {}
+if len(candidate_age_cols) > 0:
+    clinical_data = pd.read_csv(clinical_file_path, index_col=0)
+    for col in candidate_age_cols:
+        if col in clinical_data.columns:
+            age_preview[col] = clinical_data[col].head().tolist()
+    print("Age Column Preview:", age_preview)
+
+# Extract and preview gender columns if available 
+gender_preview = {}
+if len(candidate_gender_cols) > 0:
+    if 'clinical_data' not in locals():
+        clinical_data = pd.read_csv(clinical_file_path, index_col=0)
+    for col in candidate_gender_cols:
+        if col in clinical_data.columns:
+            gender_preview[col] = clinical_data[col].head().tolist()
+    print("Gender Column Preview:", gender_preview)
+# Previous execution output contained dictionaries of age and gender column candidates
+# Set chosen column names for demographic information based on that data 
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+# Print the chosen columns
+print(f"Selected Age Column: {age_col}")
+print(f"Selected Gender Column: {gender_col}")
+# Get paths
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)")
+clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load data
+clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+# Extract clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# Normalize gene symbols
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+
+# Save normalized gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+linked_data = pd.merge(
+    selected_clinical_df, 
+    normalized_gene_df.T,
+    left_index=True,
+    right_index=True
+)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Check for bias and remove biased demographic features
+is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=cleaned_data,
+    note="This dataset contains TCGA melanoma data with normalized gene expression values"
+)
+
+# Save processed data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    cleaned_data.to_csv(out_data_file)

p3/preprocess/Melanoma/cohort_info.json

@@ -0,0 +1 @@
+{"GSE261347": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE244984": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE215868": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 86, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE202806": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE200904": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 341, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE157738": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE148949": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains only reference samples from cell lines. No trait data available for analysis."}, "GSE148319": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 83, "note": "Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."}, "GSE146264": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Failed to extract gene expression data from matrix file."}, "GSE144296": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 474, "note": "This dataset contains TCGA melanoma data with normalized gene expression values"}}

p3/preprocess/Melanoma/gene_data/GSE144296.csv

@@ -0,0 +1,2 @@
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p3/preprocess/Melanoma/gene_data/GSE146264.csv

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p3/preprocess/Melanoma/gene_data/GSE148319.csv

@@ -0,0 +1,11 @@
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p3/preprocess/Melanoma/gene_data/TCGA.csv

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