p3/preprocess/Mitochondrial_Disorders/code/GSE22651.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mitochondrial_Disorders"
cohort = "GSE22651"

# Input paths
in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE22651"

# Output paths
out_data_file = "./output/preprocess/3/Mitochondrial_Disorders/GSE22651.csv"
out_gene_data_file = "./output/preprocess/3/Mitochondrial_Disorders/gene_data/GSE22651.csv"
out_clinical_data_file = "./output/preprocess/3/Mitochondrial_Disorders/clinical_data/GSE22651.csv"
json_path = "./output/preprocess/3/Mitochondrial_Disorders/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)
# Check gene expression data availability
is_gene_available = True  # Based on background info showing Illumina HT12 v3 chips were used

# Analyze trait availability
# From background info, we know this is a Friedreich's ataxia study where control and disease samples are compared
# Looking at the sample characteristics, we can identify disease status from cell lines
# Cell lines 3816.5, 4078.1A2, 4078.1B3 are FRDA patient-derived iPSC lines
trait_row = 0  # Cell line info is in row 0
def convert_trait(x):
    if pd.isna(x):
        return None
    x = x.split(': ')[1]
    if any(p in x for p in ['3816.5', '4078.1A2', '4078.1B3']):
        return 1  # Patient
    return 0  # Control

# Analyze age availability
age_row = 0  # Age info appears in row 0
def convert_age(x):
    if pd.isna(x):
        return None
    try:
        age = x.split(': ')[1]
        return float(age.split()[0])  # Extract numeric value before 'years'
    except:
        return None

# Analyze gender availability
gender_row = 0  # Gender info appears in multiple rows
def convert_gender(x):
    if pd.isna(x):
        return None
    x = x.split(': ')[1].lower()
    if 'female' in x:
        return 0
    elif 'male' in x:
        return 1
    return None

# Validate and save cohort 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 if trait data is available
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,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(selected_clinical_df)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical features
    selected_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])
# ILMN_ prefix indicates these are Illumina probe IDs from BeadArray technology
# They need to be mapped to standard gene symbols
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))
# Map probe IDs to gene symbols
# Looking at annotation data, 'ID' contains probe IDs matching ILMN_ format in gene expression data
# 'Symbol' contains gene symbols
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# Convert probe measurements to gene expression using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview result
print("Shape of gene expression data:", gene_data.shape)
print("\nExample gene expression values:")
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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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)