p3/preprocess/Celiac_Disease/code/GSE72625.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE72625"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE72625"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE72625.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE72625.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE72625.csv"
json_path = "./output/preprocess/3/Celiac_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene expression data availability
# Background info mentions "gene expression microarray", so gene data is available
is_gene_available = True

# 2. Variable availability and data type conversion
# 2.1 Data availability 
trait_row = 0  # Disease state contains celiac vs control info
age_row = None # Age not available 
gender_row = None # Gender not available

# 2.2 Data type conversion
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    # Return 1 for celiac disease, 0 for controls
    if 'celiac disease' in value:
        return 1
    elif 'healthy controls' in value:
        return 0
    return None  # CVID patients are not relevant for our analysis

convert_age = None  # No age data
convert_gender = None  # No gender data

# 3. Save metadata
validate_and_save_cohort_info(is_final=False,
                            cohort=cohort,
                            info_path=json_path, 
                            is_gene_available=is_gene_available,
                            is_trait_available=trait_row is not None)

# 4. Clinical feature extraction
# Since trait_row is not None, we need to extract clinical features
selected_clinical_df = geo_select_clinical_features(clinical_data, 
                                                  trait=trait,
                                                  trait_row=trait_row,
                                                  convert_trait=convert_trait)

# Preview the extracted features
preview_dict = preview_df(selected_clinical_df)
print("Preview of selected clinical features:")
print(preview_dict)

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Gene identifiers start with 'ILMN_' which indicates these are Illumina probe IDs
# These need to be mapped to standard human gene symbols for downstream analysis
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Additional filtering to exclude control probes 
gene_metadata = gene_metadata[gene_metadata['Species'] != 'ILMN Controls']

# Preview filtered annotation data
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify relevant columns
# Gene expression data uses probe IDs in 'ID' column
# Gene symbols are in 'Symbol' column of annotation data

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

# 3. Apply gene mapping to convert probe-level data to gene expression
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview converted gene expression data
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

# 3. Handle missing values 
linked_data = handle_missing_values(linked_data, trait)

# 4. Check and handle biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "Gene expression data from duodenal biopsies of celiac disease patients and healthy controls. Contains binary trait data (celiac disease vs control), but no age or gender information."
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data if usable and preview
if is_usable:
    print("\nFinal linked data shape:", linked_data.shape)
    print("\nPreview of linked data:")
    print(preview_df(linked_data))
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)