p3/preprocess/Post-Traumatic_Stress_Disorder/code/GSE81761.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Post-Traumatic_Stress_Disorder"
cohort = "GSE81761"

# Input paths
in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE81761"

# Output paths
out_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/GSE81761.csv"
out_gene_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/gene_data/GSE81761.csv"
out_clinical_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/clinical_data/GSE81761.csv"
json_path = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/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
# From background info, we see gene expression data using HG-U133_Plus_2 Affymetrix chip
is_gene_available = True

# 2. Variable Availability and Row IDs
# Trait (PTSD): Row 1 has case/control info 
trait_row = 1
# Age: Row 5 has age data
age_row = 5  
# Gender: Row 4 has sex data
gender_row = 4

# Convert functions
def convert_trait(x: str) -> int:
    """Convert PTSD status to binary"""
    if pd.isna(x):
        return None
    value = x.split(': ')[1].strip()
    if value == 'PTSD':
        return 1
    elif value == 'No PTSD': 
        return 0
    return None

def convert_age(x: str) -> float:
    """Convert age to float"""
    if pd.isna(x):
        return None
    try:
        return float(x.split(': ')[1])
    except:
        return None

def convert_gender(x: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if pd.isna(x):
        return None
    value = x.split(': ')[1].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. Extract clinical features
clinical_df = geo_select_clinical_features(clinical_data,
                                         trait=trait,
                                         trait_row=trait_row,
                                         convert_trait=convert_trait,
                                         age_row=age_row,
                                         convert_age=convert_age, 
                                         gender_row=gender_row,
                                         convert_gender=convert_gender)

# Preview the extracted features
preview_result = preview_df(clinical_df)
print("Preview of clinical data:")
print(preview_result)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# 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 examination of the identifiers (e.g., '1007_s_at', '1053_at'), these are Affymetrix probe IDs, not gene symbols
# They need to be mapped to standard human gene symbols for 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))
# Get probe-to-gene mapping
# 'ID' column has probe identifiers matching gene expression data
# 'Gene Symbol' column has gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply gene mapping to convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Print info about the mapping
print(f"Original probe count: {len(genetic_data)}")
print(f"Gene count after mapping: {len(gene_data)}")

# Preview the mapped gene expression data
print("\nPreview of gene expression data:")
print(gene_data.head())
# 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)