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@@ -1433,3 +1433,21 @@ p3/preprocess/Adrenocortical_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge
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p3/preprocess/Acute_Myeloid_Leukemia/gene_data/TCGA.csv

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p3/preprocess/Allergies/GSE185658.csv

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p3/preprocess/Alopecia/GSE148346.csv

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p3/preprocess/Alopecia/GSE66664.csv

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p3/preprocess/Alopecia/code/GSE18876.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE18876"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE18876"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alopecia/GSE18876.csv"
+out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/GSE18876.csv"
+out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/GSE18876.csv"
+json_path = "./output/preprocess/3/Alopecia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Gene expression data availability
+# Yes - the series title and summary mention transcriptional profiling using exon arrays
+is_gene_available = True
+
+# Variable rows and conversion functions
+trait_row = None  # Cannot reliably determine alopecia status from characteristics
+age_row = 0  # Age is in feature 0
+gender_row = None  # Not needed since all samples are male based on background info
+
+def convert_age(value):
+    if not value or ':' not in value:
+        return None
+    try:
+        age = int(value.split(':')[1].strip())
+        return age
+    except:
+        return None
+
+# Note: trait and gender conversion functions not needed since data not available
+convert_trait = None  
+convert_gender = None
+
+# Save metadata for initial filtering
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# Skip clinical feature extraction since trait data is not available
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns
+print("\nSample mapping columns ('ID' and gene_assignment):")
+print(gene_annotation[['ID', 'gene_assignment']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'gene_assignment' column: Gene information")
+# Get mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# Apply gene mapping to convert probe IDs to gene symbols
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Normalize gene symbols 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Print shape and preview to verify mapping
+print("Shape after mapping:", gene_data.shape)
+print("\nPreview of mapped gene expression data:")
+print(gene_data.head())
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Since trait data is not available, mark dataset as unusable
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,  
+    is_biased=True,  
+    df=gene_data,
+    note="Dataset lacks trait information required for analysis"
+)

p3/preprocess/Alopecia/code/GSE66664.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE66664"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE66664"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alopecia/GSE66664.csv"
+out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/GSE66664.csv"
+out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/GSE66664.csv"
+json_path = "./output/preprocess/3/Alopecia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains transcriptome data (gene expression)
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (Alopecia) can be determined from cell line
+trait_row = 0
+def convert_trait(value):
+    if not value or ':' not in value:
+        return None
+    # Extract part after colon and strip whitespace
+    val = value.split(':')[1].strip()
+    # BAN (non-balding) = 0, BAB (balding) = 1
+    if val == 'BAN':
+        return 0
+    elif val == 'BAB':
+        return 1
+    return None
+
+# Age data not available 
+age_row = None
+convert_age = None
+
+# Gender data not available (all male based on background info but this is constant)
+gender_row = None  
+convert_gender = None
+
+# 3. Save Metadata
+# is_trait_available = True since trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=True)
+
+# 4. Clinical Feature Extraction
+# Extract clinical features since trait data is available
+clinical_df = geo_select_clinical_features(clinical_data,
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait,
+                                         age_row=age_row,
+                                         convert_age=convert_age,
+                                         gender_row=gender_row,
+                                         convert_gender=convert_gender)
+
+# Preview the extracted features
+preview_result = preview_df(clinical_df)
+print("Preview of clinical data:")
+print(preview_result)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers starting with "ILMN_" indicate these are Illumina probes/beads
+# They need to be mapped to standard human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns 
+print("\nSample mapping columns ('ID' and 'Symbol'):")
+print(gene_annotation[['ID', 'Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Symbol' column: Gene information")
+# Get gene mapping between probes and genes
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply the mapping to convert probe-level data to gene expression data 
+mapped_gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview the shape and first few rows
+print("Shape of mapped gene expression data:", mapped_gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(mapped_gene_data.head())
+print("\nFirst few gene names:")
+print(mapped_gene_data.index[:20])
+
+# Save gene expression data
+mapped_gene_data.to_csv(out_gene_data_file)
+
+# Update gene_data to use mapped values
+gene_data = mapped_gene_data
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alopecia/code/GSE81071.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE81071"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE81071"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alopecia/GSE81071.csv"
+out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/GSE81071.csv"
+out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/GSE81071.csv"
+json_path = "./output/preprocess/3/Alopecia/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, this is gene expression data from RNA as mentioned in Series_overall_design
+is_gene_available = True
+
+# 2.1 Data Availability
+trait_row = 1  # disease state indicates Alopecia status through DLE/sCLE which cause alopecia
+age_row = None  # Age information not available
+gender_row = None  # Gender information not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert disease state to binary value:
+    1 for DLE/SCLE (presence of lupus with alopecia)
+    0 for healthy/normal (control)
+    """
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if value in ['dle', 'scle']:
+        return 1
+    elif value in ['healthy', 'normal']:
+        return 0
+    return None
+
+# Since age and gender data not available, their conversion functions not needed
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+is_initial = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# These identifiers (e.g. '100009613_at', '100009676_at') are Affymetrix probe IDs
+# They need to be mapped to human gene symbols for proper analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Examine all columns to identify gene symbol information
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nAll column names:")
+print(gene_annotation.columns.tolist())
+
+# Print a few complete rows to see all available information
+print("\nFirst few complete rows:")
+print(gene_annotation.head(3).to_string())
+
+# Print out some useful statistics
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Since this printout may be needed for next steps
+print("\nNote: Gene mapping will need probe IDs and gene symbols")
+print("Currently found columns:")
+print("'ID' column: Contains probe identifiers")
+print("Will need to identify appropriate column for gene symbols")
+# Get probe ID to Entrez ID mapping from annotation data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+# Use NCBI's Entrez Gene IDs to map to gene symbols 
+mapped_gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Normalize gene symbols in the index to standardize and aggregate values
+gene_data = normalize_gene_symbols_in_index(mapped_gene_data)
+
+# Save processed gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Reload clinical data from source
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Extract clinical features with the corrected trait conversion
+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
+)
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Determine if features are biased 
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note="Gene expression data successfully mapped and linked with clinical features"
+)
+
+# Save linked data only if usable AND trait is not biased
+if is_usable and not is_trait_biased:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Alopecia/code/TCGA.py

@@ -0,0 +1,30 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alopecia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Alopecia/cohort_info.json"
+
+# 1. Review subdirectories for matching trait data
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# No suitable directory exists for age-related macular degeneration
+# Mark data as unavailable 
+cohort = "TCGA_no_suitable_cohort"
+
+# Record unavailability and end preprocessing
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort, 
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)

p3/preprocess/Alopecia/gene_data/GSE148346.csv

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+size 20446260

p3/preprocess/Alopecia/gene_data/GSE18876.csv

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+oid sha256:1f138e02b190833ca491eb19bb4c1025ed390ad8e84765f26124c1c4c0a6ab1e
+size 21422913

p3/preprocess/Alopecia/gene_data/GSE66664.csv

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+size 36069801

p3/preprocess/Alopecia/gene_data/GSE81071.csv

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p3/preprocess/Alzheimers_Disease/GSE109887.csv

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p3/preprocess/Alzheimers_Disease/GSE122063.csv

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+size 25233147

p3/preprocess/Alzheimers_Disease/GSE214417.csv

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p3/preprocess/Alzheimers_Disease/clinical_data/GSE122063.csv

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p3/preprocess/Alzheimers_Disease/clinical_data/GSE132903.csv

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p3/preprocess/Alzheimers_Disease/clinical_data/GSE137202.csv

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p3/preprocess/Alzheimers_Disease/clinical_data/GSE139384.csv

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p3/preprocess/Alzheimers_Disease/clinical_data/GSE167559.csv

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p3/preprocess/Alzheimers_Disease/clinical_data/GSE185909.csv

@@ -0,0 +1,4 @@
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p3/preprocess/Alzheimers_Disease/clinical_data/GSE214417.csv

@@ -0,0 +1,4 @@
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p3/preprocess/Alzheimers_Disease/clinical_data/GSE243243.csv

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

@@ -0,0 +1,1149 @@
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p3/preprocess/Alzheimers_Disease/code/GSE109887.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE109887"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE109887"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE109887.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE109887.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE109887.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes, it contains gene expression data based on Series_overall_design mentioning "Illumina HumanHT-12 v4 BeadChip"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+
+# Trait (AD status) is available in Feature 3 
+trait_row = 3
+
+# Age is available in Feature 1
+age_row = 1 
+
+# Gender is available in Feature 0  
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(value: str) -> Optional[int]:
+    """Convert AD/Control status to binary"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'ad' in value:
+        return 1
+    elif 'control' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to continuous numeric"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    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:
+        return 0
+    elif 'male' 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. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save clinical features
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the gene identifiers like 'A1BG', 'A1CF', 'A2M', 'AACS' etc,
+# these appear to be standard human gene symbols
+# No mapping needed
+requires_gene_mapping = False
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE117589.py

@@ -0,0 +1,222 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE117589"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE117589"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE117589.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE117589.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE117589.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# From series title and summary, this appears to be gene expression data from iPSC models
+is_gene_available = True
+
+# 2.1 Data Availability
+# Feature 2 contains diagnosis information - trait data
+trait_row = 2
+
+# Feature 1 contains subject info with age and gender
+age_row = 1  
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert diagnosis to binary where AD=1, normal=0"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if "alzheimer" in value:
+        return 1
+    elif "normal" in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Extract age from subject info"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    # Extract number from strings like "60F", "72M"
+    try:
+        age = float(value[:-1])  # Remove last character (F/M) and convert to float
+        return age
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary where male=1, female=0"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    # Extract gender from strings like "60F", "72M"
+    if value.endswith('F'):
+        return 0
+    elif value.endswith('M'):
+        return 1
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. 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 clinical data
+preview_result = preview_df(clinical_df)
+print("Clinical data preview:", preview_result)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# These are ENSEMBL gene identifiers with "_at" suffix, not standard human gene symbols
+# They need to be mapped to human gene symbols for better interpretability
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Function to extract gene symbols from description field
+def extract_gene_from_description(desc):
+    if pd.isna(desc):
+        return None
+    # Extract text before [Source:HGNC Symbol;Acc:...]
+    match = re.match(r'^(.*?)\s+\[Source:', desc)
+    if match:
+        return match.group(1)
+    return None
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Extract gene symbols and add as new column
+gene_annotation['GENE_SYMBOL'] = gene_annotation['Description'].apply(extract_gene_from_description)
+
+print("\nSample mapping information:")
+print("First few rows showing ID to Gene Symbol mapping:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head(10).to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers")
+print("'GENE_SYMBOL' column: Extracted gene names from Description")
+# Create mapping dataframe between probe IDs and 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(gene_data, mapping_df)
+
+# Preview results
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE122063.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE122063"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE122063"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE122063.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE122063.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE122063.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on the series title and design, this is a microarray gene expression study
+is_gene_available = True
+
+# 2.1 Data Availability & 2.2 Data Type Conversion
+# Trait data is in Feature 0 and has multiple values
+trait_row = 0
+
+def convert_trait(value):
+    if not value or ':' not in value:
+        return None
+    diagnosis = value.split(': ')[1].lower()
+    if "alzheimer" in diagnosis:
+        return 1
+    elif "control" in diagnosis:
+        return 0
+    return None
+
+# Age data is in Feature 6
+age_row = 6
+
+def convert_age(value):
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(': ')[1])
+    except:
+        return None
+
+# Gender data is in Feature 5 
+gender_row = 5
+
+def convert_gender(value):
+    if not value or ':' not in value:
+        return None
+    gender = value.split(': ')[1].lower()
+    if gender == 'female':
+        return 0
+    elif gender == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the clinical features
+preview_result = preview_df(clinical_features)
+print("Preview of clinical features:", preview_result)
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the data shown, the gene identifiers are not human gene symbols
+# They appear to be simple numeric IDs (4, 5, 6, 7, etc.) which need mapping to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns 
+print("\nSample mapping information:")
+print("ID -> GENE_SYMBOL mapping examples:")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'GENE_SYMBOL' column: Gene symbols")
+# Get mapping between probe IDs and gene symbols from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print info about gene data 
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE132903.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE132903"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE132903"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE132903.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE132903.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE132903.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Check gene expression data availability
+# Based on background info mentioning "RNA expression" and "Illumina Human HT-12 v4 arrays"
+is_gene_available = True
+
+# Define row indices and conversion functions for clinical features
+trait_row = 3  # diagnosis info in row 3
+age_row = 2    # age info in row 2 
+gender_row = 1 # gender info in row 1
+
+def convert_trait(x):
+    """Convert diagnosis to binary: 0=ND (control), 1=AD"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip().upper()
+    if value == 'AD':
+        return 1
+    elif value == 'ND':
+        return 0
+    return None
+
+def convert_age(x):
+    """Convert age to continuous numeric value"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    if value.endswith('+'):
+        # For 90+, use 90 as conservative estimate
+        return float(value[:-1])
+    if value.replace('.','').isdigit():
+        return float(value)
+    return None
+
+def convert_gender(x):
+    """Convert gender to binary: 0=female, 1=male"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# Save initial validation 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
+)
+
+# Extract clinical features if trait data available
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed clinical data
+    preview = preview_df(clinical_features)
+    print("Clinical data preview:", preview)
+    
+    # Save clinical features
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers start with "ILMN_" which indicates these are Illumina probe IDs
+# They need to be mapped to standard human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns 
+print("\nSample mapping information:")
+print("ID -> Symbol mapping examples:")
+print(gene_annotation[['ID', 'Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers")
+print("'Symbol' column: Gene symbols")
+# Extract probe-gene mapping from annotation
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview the processed gene data
+print("Shape of gene-level data:", gene_data.shape)
+print("\nFirst few rows of gene data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE137202.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE137202"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE137202"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE137202.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE137202.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE137202.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# From background info, this dataset uses Affymetrix PrimeView arrays for whole-genome expression profiling
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# For trait, we can use genotype info from Feature 1 - distinguishes AD mutants from wild type
+trait_row = 1
+# Age and gender not available - this is a cell line study
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert genotype info to binary trait (AD mutation = 1, wild type = 0)"""
+    if not value or ':' not in value:
+        return None
+    genotype = value.split(': ')[1].lower()
+    if 'wild type' in genotype:
+        return 0
+    elif 'mutated' in genotype:  # Both APP and PSEN1 mutations are AD mutations
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3. Save Metadata
+# Initial filtering - only checking data availability at this stage
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we extract clinical features
+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
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# These identifiers are from Affymetrix microarray probes (e.g. "11715100_at")
+# They need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns 
+print("\nSample mapping information:")
+print("ID -> Gene Symbol mapping examples:")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers")
+print("'Gene Symbol' column: Gene symbols")
+# 1. Extract gene mapping columns from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Apply gene mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# 3. Normalize gene symbols to ensure consistency across different datasets
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Print gene data shape and preview
+print("\nShape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE139384.py

@@ -0,0 +1,215 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE139384"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE139384"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE139384.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE139384.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE139384.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info, this is a microarray study using HumanHT-12 v4 Expression BeadChip
+# which contains gene expression data
+is_gene_available = True
+
+# 2.1 Data Availability
+# trait_row = 1: 'clinical phenotypes' contains AD vs Control 
+# age_row = 2: contains age information
+# gender_row = 1: contains gender information
+trait_row = 1
+age_row = 2  
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if pd.isna(value):
+        return None
+    if "clinical phenotypes:" not in value:
+        return None
+    value = value.split("clinical phenotypes:")[1].strip()
+    if value == "Alzheimer`s Disease":
+        return 1
+    elif value == "Healthy Control":
+        return 0
+    return None
+
+def convert_age(value):
+    if pd.isna(value):
+        return None
+    if "age:" not in value:
+        return None
+    try:
+        age = float(value.split("age:")[1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if pd.isna(value):
+        return None
+    if "gender:" not in value:
+        return None
+    value = value.split("gender:")[1].strip()
+    if value == "Female":
+        return 0
+    elif value == "Male":
+        return 1
+    return None
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=True
+)
+
+# 4. Extract clinical features
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted clinical data
+print(preview_df(selected_clinical_df))
+
+# Save clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the probe IDs like ILMN_1343291, these are Illumina BeadArray probe IDs
+# which need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns
+print("\nSample mapping information:")
+print("ID -> Symbol mapping examples:")
+print(gene_annotation[['ID', 'Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Symbol' column: Gene symbols")
+# 1. Get mapping dataframe from gene annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 2. Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Normalize gene symbols using standard names from NCBI
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Preview results
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE167559.py

@@ -0,0 +1,107 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE167559"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE167559"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE167559.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE167559.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE167559.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info, this is a miRNA expression dataset, not gene expression
+is_gene_available = False
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# trait can be inferred from diagnosis field (1)
+trait_row = 1
+# age is in row 2
+age_row = 2
+# gender is in row 3 labeled as "Sex"
+gender_row = 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert diagnosis to binary for AD vs non-AD"""
+    if not isinstance(x, str):
+        return None
+    val = x.split(': ')[-1].strip().upper()
+    # NPH is a non-AD dementia
+    if val == 'NPH':
+        return 0
+    return None
+
+def convert_age(x):
+    """Convert age to continuous numeric"""
+    if not isinstance(x, str):
+        return None
+    try:
+        return float(x.split(': ')[-1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender to binary (0=female, 1=male)"""
+    if not isinstance(x, str):
+        return None
+    val = x.split(': ')[-1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_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
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)

p3/preprocess/Alzheimers_Disease/code/GSE185909.py

@@ -0,0 +1,222 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE185909"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE185909"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE185909.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE185909.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE185909.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# Gene expression data availability
+# From the background info, we can see this dataset uses Nimblegen expression arrays for human frontal cortex
+# This indicates it contains gene expression data
+is_gene_available = True
+
+# Clinical feature variables
+# From the sample characteristics, diagnosis info is in Feature 0 
+trait_row = 0 
+
+# Gender info is in Feature 1
+gender_row = 1
+
+# Age info is in Feature 2 
+age_row = 2
+
+def convert_trait(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1]
+    # Convert diagnosis to binary (AD vs non-AD)
+    if value == 'ad':
+        return 1
+    elif value in ['mci', 'nci']:
+        return 0
+    return None
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    value = value.lower().split(': ')[-1]
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+def convert_age(value):
+    if not isinstance(value, str):
+        return None
+    try:
+        # Extract numeric value after colon
+        age = float(value.split(': ')[-1])
+        return age
+    except:
+        return None
+
+# 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
+)
+
+# Extract clinical features since trait_row is not None
+clinical_df = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview_dict = preview_df(clinical_df)
+print("Preview of clinical features:")
+print(preview_dict)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers (e.g. AB000409, AB000463 etc),
+# these appear to be GenBank accession numbers rather than human gene symbols
+# These will need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns
+print("\nSample mapping information:")
+print("ID -> Description mapping examples:")
+print(gene_annotation[['ID', 'DESCRIPTION']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'DESCRIPTION' column: Gene descriptions containing gene names")
+# Get gene mapping dataframe using ID and DESCRIPTION columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='DESCRIPTION') 
+
+# Extract gene symbols using the function (which handles messy descriptions)
+mapping_df['Gene'] = mapping_df['Gene'].apply(extract_human_gene_symbols)
+
+# Explode lists of gene symbols to get one-to-many mapping
+mapping_df = mapping_df.explode('Gene')
+mapping_df = mapping_df.dropna(subset=['Gene'])
+
+# Apply gene mapping to convert probe measurements to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Normalize gene symbols to standard HGNC symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Print shape and preview to verify the mapping
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of mapped gene expression data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )

p3/preprocess/Alzheimers_Disease/code/GSE214417.py

@@ -0,0 +1,205 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alzheimers_Disease"
+cohort = "GSE214417"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alzheimers_Disease"
+in_cohort_dir = "../DATA/GEO/Alzheimers_Disease/GSE214417"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alzheimers_Disease/GSE214417.csv"
+out_gene_data_file = "./output/preprocess/3/Alzheimers_Disease/gene_data/GSE214417.csv"
+out_clinical_data_file = "./output/preprocess/3/Alzheimers_Disease/clinical_data/GSE214417.csv"
+json_path = "./output/preprocess/3/Alzheimers_Disease/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Gene expression data likely available based on brain tissue study
+
+# 2.1 Data Availability
+trait_row = 5  # APP+PSEN genotype indicates AD model status
+age_row = 3  # Age information available
+gender_row = 2  # Gender information available but constant (all male)
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str) -> int:
+    """Convert genotype to binary AD status"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    if '+ APP + PSEN' in value:
+        return 1  # AD model
+    elif '- APP - PSEN' in value:
+        return 0  # Control
+    return None
+
+def convert_age(x: str) -> float:
+    """Convert age to continuous months"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    try:
+        return float(value.split()[0])  # Extract number before "months"
+    except:
+        return None
+        
+def convert_gender(x: str) -> int:
+    """Convert gender to binary"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    if value.lower() == 'male':
+        return 1
+    elif value.lower() == 'female':
+        return 0
+    return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False, 
+                                cohort=cohort,
+                                info_path=json_path,
+                                is_gene_available=is_gene_available,
+                                is_trait_available=is_trait_available)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row, 
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    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)
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Examining the gene identifiers
+# The identifiers are just numeric indices (1,2,3...) and not gene symbols
+# This indicates we need to map these IDs to actual gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file and get meaningful data 
+gene_annotation = get_gene_annotation(soft_file)
+
+# Preview gene annotation data
+print("Gene annotation shape:", gene_annotation.shape)
+print("\nGene annotation preview:")
+print(preview_df(gene_annotation))
+
+print("\nNumber of non-null values in each column:")
+print(gene_annotation.count())
+
+# Print example rows showing the mapping columns
+print("\nSample mapping columns ('ID' and 'GENE_SYMBOL'):")
+print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers")
+print("'GENE_SYMBOL' column: Gene symbols")
+# Get gene mapping from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print shape and preview to verify transformation
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save normalized gene data
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+try:
+    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine if features are biased
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note="Gene expression data successfully mapped and linked with clinical features"
+    )
+
+    # 6. Save linked data only if usable AND trait is not biased
+    if is_usable and not is_trait_biased:
+        linked_data.to_csv(out_data_file)
+
+except Exception as e:
+    print(f"Error in data linking and processing: {str(e)}")
+    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=True,
+        df=pd.DataFrame(),
+        note=f"Data processing failed: {str(e)}"
+    )