p3/preprocess/Mitochondrial_Disorders/code/GSE30933.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mitochondrial_Disorders"
cohort = "GSE30933"

# Input paths
in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE30933"

# Output paths
out_data_file = "./output/preprocess/3/Mitochondrial_Disorders/GSE30933.csv"
out_gene_data_file = "./output/preprocess/3/Mitochondrial_Disorders/gene_data/GSE30933.csv"
out_clinical_data_file = "./output/preprocess/3/Mitochondrial_Disorders/clinical_data/GSE30933.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)
# 1. Gene Expression Data Availability
# Based on background info mentioning gene expression studies and microarray experiments
is_gene_available = True 

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# Trait (disease status) is in row 0
trait_row = 0

# Age and gender not available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert disease status to binary (0=control, 1=FRDA)"""
    if not isinstance(x, str):
        return None
    value = x.split(": ")[-1].strip()
    if value == "FRDA":
        return 1
    elif value == "Normal":
        return 0
    return None  # Carriers excluded

def convert_age(x):
    """Placeholder since age not available"""
    return None

def convert_gender(x):
    """Placeholder since gender not available"""
    return None

# 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
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                         age_row, convert_age,
                                         gender_row, convert_gender)

# Preview and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))

# Save to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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])
# The gene identifiers start with "ILMN_", which indicates these are Illumina probe IDs
# and 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))
# 1. Based on preview, 'ID' is the identifier column matching to the gene expression data
# and 'SYMBOL' is the gene symbol column

# 2. Get mapping dataframe with ID and SYMBOL columns
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'SYMBOL')

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

# Preview output
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_data))

# Save gene data to file
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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)