p3/preprocess/Polycystic_Ovary_Syndrome/code/GSE43322.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Polycystic_Ovary_Syndrome"
cohort = "GSE43322"

# Input paths
in_trait_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome"
in_cohort_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome/GSE43322"

# Output paths
out_data_file = "./output/preprocess/3/Polycystic_Ovary_Syndrome/GSE43322.csv"
out_gene_data_file = "./output/preprocess/3/Polycystic_Ovary_Syndrome/gene_data/GSE43322.csv"
out_clinical_data_file = "./output/preprocess/3/Polycystic_Ovary_Syndrome/clinical_data/GSE43322.csv"
json_path = "./output/preprocess/3/Polycystic_Ovary_Syndrome/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info mentioning "gene expression" and "adipose tissue", this is likely gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 3  # condition: PCOS or control
age_row = 1    # age available
gender_row = 0 # gender available but constant (all Female)

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert PCOS status to binary"""
    if pd.isna(value):
        return None
    value = value.split(": ")[-1].lower()
    if "pcos" in value:
        return 1
    elif "control" in value:
        return 0
    return None

def convert_age(value):
    """Convert age to continuous value"""
    if pd.isna(value):
        return None
    try:
        # Extract number after colon
        age = float(value.split(": ")[-1])
        return age
    except:
        return None

def convert_gender(value):
    """Convert gender to binary (though all female in this case)"""
    if pd.isna(value):
        return None
    value = value.split(": ")[-1].lower()
    if "female" in value:
        return 0
    elif "male" in value:
        return 1
    return None

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

# 4. Clinical Feature Extraction
clinical_features = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row,
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview and save clinical features
print("Preview of clinical features:")
print(preview_df(clinical_features))

clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Based on the ID format (e.g. "100009676_at", "10001_at"), these look like probe IDs from a microarray
# rather than standard human gene symbols. They need to be mapped to gene symbols for proper analysis.

requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. Based on observation:
# - Gene expression data uses IDs like "100009676_at"
# - Gene annotation data has matching IDs in "ID" column
# - Gene symbols are stored in "ORF" column

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')

# 3. Convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview results
print("Shape of gene expression data:", gene_data.shape)
print("\nPreview of gene data:")
print(preview_df(gene_data))
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

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

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
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 only if usable 
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)