p3/preprocess/Anxiety_disorder/code/GSE94119.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE94119"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE94119"

# Output paths
out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE94119.csv"
out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE94119.csv"
out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE94119.csv"
json_path = "./output/preprocess/3/Anxiety_disorder/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
# This is likely gene expression data since:
# - Uses Illumina HT-12v4 BeadChip microarray
# - Measures whole blood RNA
# - For genome-wide expression profiles
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Keys
trait_row = 2  # Can infer trait status from timepoint
age_row = None  # Age not available in characteristics
gender_row = 0  # Gender is in feature 0

# 2.2 Conversion Functions 
def convert_trait(value):
    """Convert timepoint to binary trait status
    pre-treatment = 1 (has anxiety)
    post/follow-up = 0 (treated)
    """
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if value == 'pre':
        return 1
    elif value in ['post', 'follow-up']:
        return 0
    return None

def convert_gender(value):
    """Convert gender to binary
    female = 0, male = 1
    """
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].upper()
    if value == 'FEMALE':
        return 0
    elif value == 'MALE':
        return 1
    return None

convert_age = None  # No age data available

# 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
# Since trait_row is not None, extract clinical features
clinical_features = 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 extracted features
print(preview_df(clinical_features))

# Save clinical features
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 appear to be Illumina array probe IDs (starting with 'ILMN_')
# They need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# 1. Observe gene identifiers
# Gene expression data uses 'ILMN_' identifiers which match with 'ID' column in annotation
# Gene symbols are in 'Symbol' column of annotation

# 2. Get gene mapping dataframe with ID and Symbol columns
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe measurements to gene expression using the mapping
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
try:
    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

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

    # 5. 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"
    )

    # 6. 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)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )