p3/preprocess/Anxiety_disorder/code/GSE60491.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE60491"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE60491"

# Output paths
out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE60491.csv"
out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE60491.csv"
out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE60491.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
# Based on the background information, this dataset contains gene expression data from peripheral blood mononuclear cells
is_gene_available = True

# 2.1 Data Availability
# Trait data (anxiety) needs to be inferred from neuroticism score
trait_row = 12  # neuroticism data
age_row = 0     # age data available 
gender_row = 1  # gender data available (male: 0/1)

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert neuroticism score to binary anxiety trait.
    High neuroticism (>1.0) is considered as having anxiety disorder"""
    if not value or 'neuroticism:' not in value:
        return None
    try:
        score = float(value.split(':')[1])
        return 1 if score > 1.0 else 0
    except:
        return None

def convert_age(value):
    """Convert age string to integer"""
    if not value or 'age:' not in value:
        return None
    try:
        return int(value.split(':')[1])
    except:
        return None

def convert_gender(value):
    """Convert gender encoding where male:1 is kept as 1, male:0 means female so returns 0"""
    if not value or 'male:' not in value:
        return None
    try:
        return int(value.split(':')[1])
    except:
        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
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 and save clinical data
print("Preview of extracted clinical features:")
print(preview_df(selected_clinical_df))
selected_clinical_df.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)
# Check if the identifiers look like standard human gene symbols
# From the sample data, we can see identifiers like "A1BG", "A1CF", "A2M" which are standard human gene symbols
# Most identifiers follow the pattern of uppercase letters and numbers typical of HGNC symbols
# There are no probe IDs (like Affymetrix probe IDs) that would need mapping

requires_gene_mapping = False
# 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)}"
    )