p3/preprocess/Alopecia/code/GSE81071.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Alopecia"
cohort = "GSE81071"

# Input paths
in_trait_dir = "../DATA/GEO/Alopecia"
in_cohort_dir = "../DATA/GEO/Alopecia/GSE81071"

# Output paths
out_data_file = "./output/preprocess/3/Alopecia/GSE81071.csv"
out_gene_data_file = "./output/preprocess/3/Alopecia/gene_data/GSE81071.csv"
out_clinical_data_file = "./output/preprocess/3/Alopecia/clinical_data/GSE81071.csv"
json_path = "./output/preprocess/3/Alopecia/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Yes, this is gene expression data from RNA as mentioned in Series_overall_design
is_gene_available = True

# 2.1 Data Availability
trait_row = 1  # disease state indicates Alopecia status through DLE/sCLE which cause alopecia
age_row = None  # Age information not available
gender_row = None  # Gender information not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert disease state to binary value:
    1 for DLE/SCLE (presence of lupus with alopecia)
    0 for healthy/normal (control)
    """
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if value in ['dle', 'scle']:
        return 1
    elif value in ['healthy', 'normal']:
        return 0
    return None

# Since age and gender data not available, their conversion functions not needed
convert_age = None
convert_gender = None

# 3. Save Metadata
is_trait_available = trait_row is not None
is_initial = 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
if trait_row is not None:
    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 the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# These identifiers (e.g. '100009613_at', '100009676_at') are Affymetrix probe IDs
# They need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Examine all columns to identify gene symbol information
print("Gene annotation shape:", gene_annotation.shape)
print("\nAll column names:")
print(gene_annotation.columns.tolist())

# Print a few complete rows to see all available information
print("\nFirst few complete rows:")
print(gene_annotation.head(3).to_string())

# Print out some useful statistics
print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Since this printout may be needed for next steps
print("\nNote: Gene mapping will need probe IDs and gene symbols")
print("Currently found columns:")
print("'ID' column: Contains probe identifiers")
print("Will need to identify appropriate column for gene symbols")
# Get probe ID to Entrez ID mapping from annotation data
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')

# Use NCBI's Entrez Gene IDs to map to gene symbols 
mapped_gene_data = apply_gene_mapping(gene_data, mapping_df)

# Normalize gene symbols in the index to standardize and aggregate values
gene_data = normalize_gene_symbols_in_index(mapped_gene_data)

# Save processed gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data from source
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Extract clinical features with the corrected trait conversion
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
)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# Determine if features are biased 
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate and save cohort info
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=is_trait_biased,
    df=linked_data,
    note="Gene expression data successfully mapped and linked with clinical features"
)

# Save linked data only if usable AND trait is not biased
if is_usable and not is_trait_biased:
    linked_data.to_csv(out_data_file)