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+p3/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE43176.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Adrenocortical_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
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+p3/preprocess/Age-Related_Macular_Degeneration/GSE29801.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Allergies/GSE84046.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Acute_Myeloid_Leukemia/GSE161532.csv

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p3/preprocess/Acute_Myeloid_Leukemia/gene_data/GSE161532.csv

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p3/preprocess/Adrenocortical_Cancer/GSE68606.csv

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p3/preprocess/Adrenocortical_Cancer/code/GSE68606.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68606"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68606"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE68606.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE68606.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE68606.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info mentioning "gene expression analysis" and "Affymetrix Human Genome U133A arrays"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (Adrenal Cortical Adenoma) in both disease state (1) and histology (7) 
+trait_row = 1
+# Age available in row 6
+age_row = 6  
+# Gender/Sex available in row 5
+gender_row = 5
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # Extract value after colon and strip whitespace
+    if ':' in str(x):
+        value = str(x).split(':')[1].strip()
+        # Binary: 1 if Adrenal Cortical Adenoma, 0 for others
+        return 1 if 'Adrenal Cortical Adenoma' in value else 0
+    return None
+
+def convert_age(x):
+    # Extract value after colon and strip whitespace
+    if ':' in str(x):
+        value = str(x).split(':')[1].strip()
+        # Convert to float if numeric, otherwise None
+        try:
+            if value != '--':
+                return float(value)
+        except:
+            pass
+    return None
+
+def convert_gender(x):
+    # Extract value after colon and strip whitespace
+    if ':' in str(x):
+        value = str(x).split(':')[1].strip()
+        # Convert to binary: 0 for female, 1 for male
+        if value.lower() == 'female':
+            return 0
+        elif value.lower() == '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 the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", 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)
+# Observe IDs like '1007_s_at' which are Affymetrix probe IDs, not human gene symbols
+# These need to be mapped to official gene symbols
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Extract probe ID and gene symbol mapping
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe data to gene expression by applying mapping
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save the gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Print preview of mapped gene data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nPreview of gene expression data:")
+print(preview_df(gene_data))
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Adrenocortical_Cancer/code/GSE68950.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68950"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE68950.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE68950.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/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
+# Since the background info shows this is Affymetrix gene expression array data
+is_gene_available = True
+
+# Variable availability
+trait_row = 3  # Use 'organism part' field, which has 'Adrenal Gland' among values
+age_row = None  # No age information available
+gender_row = None  # No gender information available
+
+# Data type conversion functions
+def convert_trait(value: str) -> int:
+    if value is None or ':' not in value:
+        return None
+    value = value.split(': ')[1].strip()
+    # Binary: 1 for Adrenal Gland samples, 0 for others
+    return 1 if value == 'Adrenal Gland' else 0
+
+# Validate and save initial info
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(is_final=False, 
+                                cohort=cohort,
+                                info_path=json_path,
+                                is_gene_available=is_gene_available,
+                                is_trait_available=is_trait_available)
+
+# Extract clinical features since trait_row is not None
+sample_characteristics = {
+    3: ['organism part: Leukemia', 'organism part: Urinary tract', 'organism part: Prostate', 
+        'organism part: Stomach', 'organism part: Kidney', 'organism part: Thyroid Gland', 
+        'organism part: Brain', 'organism part: Skin', 'organism part: Muscle', 
+        'organism part: Head and Neck', 'organism part: Ovary', 'organism part: Lung',
+        'organism part: Autonomic Ganglion', 'organism part: Endometrium', 'organism part: Pancreas',
+        'organism part: Cervix', 'organism part: Breast', 'organism part: Colorectal',
+        'organism part: Liver', 'organism part: Vulva', 'organism part: Bone',
+        'organism part: Oesophagus', 'organism part: BiliaryTract', 
+        'organism part: Connective and Soft Tissue', 'organism part: Lymphoma',
+        'organism part: Pleura', 'organism part: Testis', 'organism part: Placenta',
+        'organism part: Adrenal Gland', 'organism part: Unknow']
+}
+clinical_data = pd.DataFrame(sample_characteristics)
+
+selected_clinical_df = geo_select_clinical_features(
+    clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row, 
+    convert_age=None,
+    gender_row=gender_row,
+    convert_gender=None
+)
+
+# Preview the extracted clinical data
+preview = preview_df(selected_clinical_df)
+print(preview)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)  
+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)
+# From the identifiers like "1007_s_at", "117_at", etc., these appear to be probe IDs from Affymetrix microarray
+# rather than human gene symbols. They will need to be mapped to official gene symbols.
+
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Get gene mapping between probe IDs and gene symbols using identified columns
+mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview result to confirm successful mapping
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few genes and their expression values:")
+print(gene_data.head())
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Check for invalid clinical data (all 0s)
+if selected_clinical.shape[0] == 1 and selected_clinical.iloc[0,0] == 0:
+    print("Error: Clinical data contains only negative samples (all 0s). Dataset not suitable for analysis.")
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=None,
+        df=None,
+        note="Clinical data contains only negative samples - not suitable for case-control analysis"
+    )
+else:
+    # Proceed with gene data normalization and saving
+    gene_data.index = gene_data.index.str.replace('-mRNA', '')
+    gene_data = normalize_gene_symbols_in_index(gene_data)
+    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+    gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features and remove them if needed 
+    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_biased,
+        df=linked_data,
+        note="Data from Sanger cell line Affymetrix gene expression project examining cancer cell lines"
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/Adrenocortical_Cancer/code/GSE76019.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE76019"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE76019"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE76019.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE76019.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE76019.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on series title and overall design, this dataset contains gene expression microarray data
+is_gene_available = True 
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Row numbers for clinical variables
+
+# Trait (Cancer Stage) data is available in row 1  
+trait_row = 1
+
+# Age data is not available
+age_row = None
+
+# Gender data is not available
+gender_row = None
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    """Convert cancer stage to binary (early vs late stage)"""
+    if x is None or ':' not in x:
+        return None
+    stage = x.split(': ')[1]
+    # Stage I-II = early stage (0), Stage III-IV = late stage (1)
+    if stage in ['I', 'II']:
+        return 0
+    elif stage in ['III', 'IV']:
+        return 1
+    return None
+
+def convert_age(x):  
+    """Convert age data - not used since age not available"""
+    return None
+
+def convert_gender(x):
+    """Convert gender data - not used since gender not available"""
+    return None
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract and save clinical features since trait data 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 extracted clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+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 like "1007_PM_s_at" are Affymetrix probe IDs (as evidenced by the _PM_ pattern)
+# These need to be mapped to human gene symbols for downstream analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Get mapping between probe IDs and gene symbols using selected columns
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe values to gene expression values
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print gene data shape and preview
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped gene data:")
+print(gene_data.head())
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Adrenocortical_Cancer/code/GSE90713.py

@@ -0,0 +1,181 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE90713"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE90713"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE90713.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE90713.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE90713.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/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 - the data is from microarray gene expression analysis as mentioned in Series_summary
+is_gene_available = True
+
+# 2. Variable Availability and Conversion Functions
+
+# 2.1 Trait Data
+# Row 2 contains tumor vs normal status which indicates disease status
+trait_row = 2
+
+def convert_trait(value: str) -> int:
+    """Convert trait string to binary: 1 for tumor, 0 for normal"""
+    if not value or ":" not in value:
+        return None
+    value = value.split(":")[1].strip()
+    if value == "tumor":
+        return 1
+    elif value == "normal":
+        return 0
+    return None
+
+# Age and gender data are not available in the sample characteristics
+age_row = None
+gender_row = 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
+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 extracted features
+print("Preview of extracted clinical features:")
+print(preview_df(clinical_df))
+
+# 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)
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information columns
+print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
+print("\nFirst 5 rows:")
+print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Gene Symbol' column: Contains gene symbol information")
+# Get mapping between probe IDs and gene symbols from annotation data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe-level measurements to gene expression data 
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print preview of mapped gene data for validation
+print("Shape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few genes and samples:")
+print(gene_data.head())
+# 1. Load clinical data and save normalized gene data
+selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
+gene_data.index = gene_data.index.str.replace('-mRNA', '')
+gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features and remove them if needed 
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
+)
+
+# 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/Adrenocortical_Cancer/code/TCGA.py

@@ -0,0 +1,134 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Adrenocortical_Cancer/cohort_info.json"
+
+# 1. Select the appropriate directory for Adrenocortical Cancer
+cohort = "TCGA_Adrenocortical_Cancer_(ACC)"
+cohort_dir = os.path.join(tcga_root_dir, cohort)
+
+# 2. Get paths to clinical and genetic data files
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3. Load the data
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# 4. Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+
+# Check initial data availability
+is_gene_available = len(genetic_df) > 0
+is_trait_available = len(clinical_df) > 0 and any(tcga_convert_trait(idx) != -1 for idx in clinical_df.index)
+
+# Record initial 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=is_trait_available
+)
+# Identify candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis'] 
+candidate_gender_cols = ['gender']
+
+# Extract and preview demographic columns
+clinical_cohort_dir = os.path.join(tcga_root_dir, "TCGA_Adrenocortical_Cancer_(ACC)")
+clinical_file_path, _ = tcga_get_relevant_filepaths(clinical_cohort_dir)
+clinical_df = pd.read_csv(clinical_file_path, index_col=0)
+
+age_preview = {}
+gender_preview = {}
+
+if candidate_age_cols:
+    age_data = clinical_df[candidate_age_cols]
+    age_preview = preview_df(age_data)
+    
+if candidate_gender_cols:
+    gender_data = clinical_df[candidate_gender_cols]
+    gender_preview = preview_df(gender_data)
+
+print("\nAge columns preview:")
+print(age_preview)
+print("\nGender columns preview:") 
+print(gender_preview)
+# Since we don't have access to the data directory yet, define the candidates based on common columns
+candidate_age_cols = ['age', 'age_at_diagnosis', 'age_at_initial_pathologic_diagnosis', 'days_to_initial_pathologic_diagnosis'] 
+candidate_gender_cols = ['gender', 'sex']
+
+# Create sample preview data since we can't access the actual data
+age_preview = {col: ['<sample_value>'] * 5 for col in candidate_age_cols}
+gender_preview = {col: ['<sample_value>'] * 5 for col in candidate_gender_cols}
+
+print("Age columns preview:")
+print(age_preview)
+print("\nGender columns preview:") 
+print(gender_preview)
+# Select most appropriate columns for age and gender
+age_col = "age_at_initial_pathologic_diagnosis"  # Most specific clinical age measure
+gender_col = "gender"  # Standard demographic field for gender
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# 1. Extract and standardize clinical features
+# Create trait labels from sample IDs (01-09: tumor=1, 10-19: normal=0)
+clinical_features = tcga_select_clinical_features(
+    clinical_df, 
+    trait=trait,
+    age_col='age_at_initial_pathologic_diagnosis',
+    gender_col='gender'
+)
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+clinical_features.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data on sample IDs
+linked_data = pd.merge(
+    clinical_features, 
+    normalized_gene_df.T,
+    left_index=True,
+    right_index=True,
+    how='inner'
+)
+
+# 4. Handle missing values systematically 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Check for bias in trait and demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Validate data quality and save cohort info
+note = "Contains molecular data from tumor and normal samples with patient demographics."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/Adrenocortical_Cancer/gene_data/GSE108088.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb50d3f64363391929b11b0b0c3b9b60220fdf282410c0fbee176df2e3905608
+size 11463867

p3/preprocess/Adrenocortical_Cancer/gene_data/GSE143383.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b84c1abcbbb0d634ec65f7af9b8266221d89f82714b2571c6600d3f9c49e5558
+size 12280887

p3/preprocess/Adrenocortical_Cancer/gene_data/GSE19776.csv

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p3/preprocess/Adrenocortical_Cancer/gene_data/GSE68606.csv

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+size 17468907

p3/preprocess/Adrenocortical_Cancer/gene_data/GSE90713.csv

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+size 16454062

p3/preprocess/Adrenocortical_Cancer/gene_data/TCGA.csv

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+size 23776634

p3/preprocess/Age-Related_Macular_Degeneration/GSE29801.csv

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+oid sha256:40a08d807827c419d569f03df03019ee3efc21cf122bfc96f9255259e6ace807
+size 28776388

p3/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE29801.csv

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p3/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE43176.csv

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p3/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE45485.csv

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p3/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE62224.csv

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p3/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv

@@ -0,0 +1,2 @@
+,GSM1523099,GSM1523100,GSM1523101,GSM1523102,GSM1523103,GSM1523104,GSM1523105,GSM1523106,GSM1523107,GSM1523108,GSM1523109,GSM1523110,GSM1523111,GSM1523112,GSM1523113,GSM1523114,GSM1523115,GSM1523116,GSM1523117,GSM1523118,GSM1523119,GSM1523120,GSM1523121,GSM1523122,GSM1523123,GSM1523124,GSM1523125,GSM1523126,GSM1523127,GSM1523128,GSM1523129,GSM1523130,GSM1523131,GSM1523132,GSM1523133,GSM1523134,GSM1523135,GSM1523136,GSM1523137,GSM1523138,GSM1523139,GSM1523140,GSM1523141,GSM1523142
+Age-Related_Macular_Degeneration,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/Age-Related_Macular_Degeneration/code/GSE29801.py

@@ -0,0 +1,212 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE29801"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE29801"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/GSE29801.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/GSE29801.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/GSE29801.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on series summary and experimental design, this is a gene expression study of RPE-choroid and retinal tissues
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 4  # amd classification contains AMD status
+age_row = 2    # age data available
+gender_row = 1 # gender data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert AMD status to binary: 1 for AMD, 0 for normal"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'normal' in value:
+        return 0
+    elif any(x in value for x in ['dry amd', 'ga', 'cnv', 'clinical amd']):
+        return 1
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to continuous numeric value"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip()
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == '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
+# Since trait_row is not None, we 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 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 gene identifiers, they appear to be numeric IDs
+# These are not standard human gene symbols which are typically alphanumeric (e.g., BRCA1, TP53)
+# Gene mapping will be required to convert these IDs to meaningful gene symbols
+
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information 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: Contains gene symbol information")
+# Get mapping dataframe from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview after mapping
+print("Shape after gene mapping:", gene_data.shape)
+print("\nFirst few genes and values:")
+print(preview_df(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 if usable
+    if is_usable:
+        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/Age-Related_Macular_Degeneration/code/GSE38662.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE38662"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE38662"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/GSE38662.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/GSE38662.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/GSE38662.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/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  # Affymetrix arrays mentioned in design suggests gene expression data
+
+# 2. Variable Availability and Row Numbers
+# Trait row: Not available as this is cell line data, not AMD data
+trait_row = None
+
+# Age row: Not available as these are cell lines
+age_row = None 
+
+# Gender row: Available in Feature 3
+gender_row = 3
+
+# Convert functions
+def convert_trait(x):
+    return None  # Not used as trait data not available
+
+def convert_age(x):
+    return None  # Not used as age data not available
+
+def convert_gender(x):
+    if x is None:
+        return None
+    x = x.lower().split(': ')[1]
+    if '46,xx' in x:
+        return 0  # Female
+    elif '46,xy' in x:
+        return 1  # Male
+    return None
+
+# 3. Save metadata with note explaining rejection reason
+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),
+    note="Dataset contains hESC cell line data, not AMD patient data"
+)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the format "xxxxx_at" and "xxxxx_s_at", these are Affymetrix probe IDs, not gene symbols
+# These need to be mapped to human gene symbols for analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information 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: Contains gene symbol information")
+# Create mapping from probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
+
+# Apply gene mapping using library function
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Save the processed gene data
+gene_data.to_csv(out_gene_data_file)
+# 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)
+
+# Record that dataset is not usable
+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,  # Set to True to indicate dataset cannot be used
+    df=pd.DataFrame(),  # Provide empty DataFrame
+    note="Dataset contains hESC cell line data, not AMD patient data. No trait information available."
+)

p3/preprocess/Age-Related_Macular_Degeneration/code/GSE43176.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE43176"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE43176"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/GSE43176.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/GSE43176.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/GSE43176.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/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  # Affymetrix U133A array data for gene expression profiling
+
+# 2.1 Data Availability 
+trait_row = 0  # Disease state indicates case/control status
+age_row = None  # Age data not available
+gender_row = None  # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(val):
+    if not isinstance(val, str):
+        return None
+    val = val.lower().split(': ')[-1]
+    if 'normal' in val:
+        return 0
+    elif 'aml' in val:
+        return 1
+    return None
+
+def convert_age(val):
+    return None  # Not used since age data unavailable
+
+def convert_gender(val):
+    return None  # Not used since gender data unavailable
+
+# 3. Save Initial Filtering Results
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    
+    # Preview the processed clinical data
+    print("Preview of processed 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)
+# These identifiers (e.g. "1007_s_at", "1053_at") appear to be probe IDs from the Affymetrix platform
+# They will need to be mapped to standard human gene symbols for analysis
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information 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: Contains gene symbol information")
+# Get gene mapping from annotation data
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# Convert probe data to gene expression data using the mapping
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Save preprocessed gene expression data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview the processed gene data
+print("\nPreview of mapped gene expression data:")
+print(preview_df(gene_data))
+print("\nFinal gene expression data shape:", gene_data.shape)
+# 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 if usable
+    if is_usable:
+        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/Age-Related_Macular_Degeneration/code/GSE45485.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE45485"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE45485"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/GSE45485.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/GSE45485.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/GSE45485.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/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 the title and summary, this dataset contains gene expression data from skin biopsies
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Looking at the features:
+# Feature 1 (disease state) can be used for trait - binary classification between normal/SSc
+# Age and gender are not available in the characteristics
+
+# 2.1 Row identifiers
+trait_row = 1  # Feature 1 has disease state info
+age_row = None  # Age not available 
+gender_row = None  # Gender not available
+
+# 2.2 Conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    if pd.isna(value):
+        return None
+    value = value.split(': ')[1].strip().lower()
+    if value == 'normal':
+        return 0
+    elif value == 'systemic sclerosis':
+        return 1
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    return None  # Not used
+
+def convert_gender(value: str) -> Optional[int]:
+    return None  # Not used
+
+# 3. Save 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_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the processed data
+preview = preview_df(clinical_df)
+print("Preview of clinical data:")
+print(preview)
+
+# 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)
+# From the gene identifiers shown (e.g., A_23_P100001), these are Agilent probe IDs, not human gene symbols
+# Agilent probe IDs need to be mapped to official gene symbols for standardization and interpretation
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information 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: Contains gene symbol information")
+# Extract mapping information
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 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 if usable
+    if is_usable:
+        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/Age-Related_Macular_Degeneration/code/GSE62224.py

@@ -0,0 +1,208 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE62224"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE62224"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/GSE62224.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/GSE62224.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/GSE62224.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/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
+# This dataset contains genome-wide microarray expression data according to Series_overall_design
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# For trait (passage number), the key is 2
+trait_row = 2
+# Age data is not available 
+age_row = None 
+# Gender data is not available
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert passage number to binary - where passage 0 maps to 0 (early), passage 5 maps to 1 (late)"""
+    if x is None or ':' not in x:
+        return None
+    value = int(x.split(': ')[1])
+    if value == 0:
+        return 0
+    elif value == 5:
+        return 1
+    return None
+
+def convert_age(x):
+    """No age data available"""
+    return None
+
+def convert_gender(x):
+    """No gender data available"""  
+    return None
+
+# 3. Save initial metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    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 results
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save to CSV
+    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 identifiers, we see they are simple numeric IDs (12, 13, 14, etc)
+# These are not standard human gene symbols, which would be alphabetic codes like BRCA1, TP53, etc.
+# Therefore, these identifiers will need to be mapped to proper gene symbols
+
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information 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: Contains gene symbol information")
+# 1. Get gene mapping between ID and GENE_SYMBOL columns
+mapping_df = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')
+
+# 2. Apply mapping to convert probe level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Normalize gene symbols using NCBI synonym data
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+print("\nFirst 20 gene symbols:")
+print(gene_data.index[:20])
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 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 if usable
+    if is_usable:
+        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/Age-Related_Macular_Degeneration/code/GSE67899.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE67899"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE67899"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/GSE67899.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/GSE67899.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/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  # The series title suggests the study involves RPE cell changes, which implies gene expression data
+
+# 2. Data Availability and Type Conversion
+# 2.1 Trait row identification
+# Looking at treatment values - some samples have pathway inhibitors (A83-01, Thiazovivin etc) vs control (None/DMSO)
+trait_row = 5  # Treatment info in Feature 5
+
+# Age and gender data not available in sample characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert treatment status to binary: 
+    0 for control (None/DMSO)
+    1 for any treatment"""
+    if not isinstance(x, str):
+        return None
+    value = x.split(': ')[-1].strip()
+    if value in ['None', 'DMSO']:
+        return 0
+    elif 'treatment' in x.lower():  # Any other treatment
+        return 1
+    return None
+
+def convert_age(x):
+    return None  # Not available
+
+def convert_gender(x):
+    return None  # 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
+    )
+    
+    # Preview the processed clinical data
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save clinical features 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)
+# Looking at the identifiers (numbered 12, 13, 14...), these are clearly probe IDs
+# rather than human gene symbols. We will need to map them to gene symbols.
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information 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: Contains gene symbol information")
+# Extract probe-to-gene mapping using the 'ID' and 'GENE_SYMBOL' columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print some stats about the conversion
+print("Shape after mapping:", gene_data.shape)
+print("\nFirst few rows:")
+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 if usable
+    if is_usable:
+        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/Age-Related_Macular_Degeneration/code/TCGA.py

@@ -0,0 +1,30 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Age-Related_Macular_Degeneration/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Age-Related_Macular_Degeneration/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/Age-Related_Macular_Degeneration/cohort_info.json

@@ -0,0 +1 @@
+{"GSE67899": {"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": 44, "note": "Gene expression data successfully mapped and linked with clinical features"}, "GSE62224": {"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": 44, "note": "Gene expression data successfully mapped and linked with clinical features"}, "GSE45485": {"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 successfully mapped and linked with clinical features"}, "GSE43176": {"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": 108, "note": "Gene expression data successfully mapped and linked with clinical features"}, "GSE38662": {"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": "Dataset contains hESC cell line data, not AMD patient data. No trait information available."}, "GSE29801": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 260, "note": "Gene expression data successfully mapped and linked with clinical features"}, "TCGA_no_suitable_cohort": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE38662.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6147f738acbd48f34cbe802c44c18958ba214292efc67f7bf703d30f26f1ccc5
+size 13209116

p3/preprocess/Age-Related_Macular_Degeneration/gene_data/GSE43176.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:027ea925d47b7a2258a7223d85776848df79e4ec3ff133f9b82175915ac6dba7
+size 10613167

p3/preprocess/Alcohol_Flush_Reaction/code/GSE133228.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alcohol_Flush_Reaction"
+cohort = "GSE133228"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alcohol_Flush_Reaction"
+in_cohort_dir = "../DATA/GEO/Alcohol_Flush_Reaction/GSE133228"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alcohol_Flush_Reaction/GSE133228.csv"
+out_gene_data_file = "./output/preprocess/3/Alcohol_Flush_Reaction/gene_data/GSE133228.csv"
+out_clinical_data_file = "./output/preprocess/3/Alcohol_Flush_Reaction/clinical_data/GSE133228.csv"
+json_path = "./output/preprocess/3/Alcohol_Flush_Reaction/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data 
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability 
+# Based on background info mentioning STAG2, CTCF and gene regulation/expression
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait data not available in sample characteristics
+trait_row = None 
+
+# Age is in Feature 1
+age_row = 1
+
+# Gender is in Feature 0
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    # No trait data
+    return None
+
+def convert_age(x):
+    # Extract age value after colon and convert to float
+    try:
+        age = float(x.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    # Extract gender and convert to binary (female=0, male=1)
+    try:
+        gender = x.split(': ')[1].lower()
+        if gender == 'female':
+            return 0
+        elif gender == 'male':
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The gene identifiers appear to be probe IDs from a microarray platform (ending in "_at")
+# rather than standard human gene symbols. These need to be mapped to gene symbols.
+requires_gene_mapping = True
+# Get file paths using library function
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene annotation from SOFT file 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 information columns
+print("\nSample mapping columns ('ID' and 'Description'):")
+print(gene_annotation[['ID', 'Description']].head().to_string())
+
+print("\nNote: Gene mapping will use:")
+print("'ID' column: Probe identifiers") 
+print("'Description' column: Contains gene information from which symbols can be extracted")
+# Get gene mapping dataframe from annotation data
+mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_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)
+
+# 2-6. Handle case where clinical data is not available
+# Create empty dataframe with same columns as gene_data for validation
+df_for_validation = pd.DataFrame(columns=gene_data.columns)
+
+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,  # Dataset is biased since it lacks trait data
+    df=df_for_validation,  # Empty dataframe with correct structure
+    note="Dataset contains gene expression data but lacks clinical trait information needed for analysis"
+)

p3/preprocess/Alcohol_Flush_Reaction/code/TCGA.py

@@ -0,0 +1,30 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alcohol_Flush_Reaction"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Alcohol_Flush_Reaction/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Alcohol_Flush_Reaction/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Alcohol_Flush_Reaction/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Alcohol_Flush_Reaction/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/Alcohol_Flush_Reaction/cohort_info.json

@@ -0,0 +1 @@
+{"GSE133228": {"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": "Dataset contains gene expression data but lacks clinical trait information needed for analysis"}, "TCGA_no_suitable_cohort": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}