p3/preprocess/Asthma/code/GSE185658.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE185658"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE185658"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE185658.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE185658.csv"
json_path = "./output/preprocess/3/Asthma/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 dataset contains microarray data for gene expression (see Series_summary)
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability and Location
trait_row = 1  # 'group' feature contains asthma status
age_row = None  # Age data not available
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert asthma status to binary: 1 for asthma, 0 for healthy"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if 'Asthma' in value:  # Both AsthmaHDM and AsthmaHDMNeg are asthma cases
        return 1
    elif value == 'Healthy':
        return 0
    return None

convert_age = None  # No age data
convert_gender = None  # No gender data

# 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
if trait_row is not None:
    # Extract clinical features
    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
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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)
# The identifiers are numeric IDs like '7892501', not standard gene symbols
# These appear to be probe IDs from a microarray platform that need to be mapped to gene symbols
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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. Identify mapping columns from gene annotation
# 'ID' column in annotation matches probe IDs in gene expression data
# 'gene_assignment' contains gene symbols and annotations
prob_col = 'ID'
gene_col = 'gene_assignment'

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Print the shape and preview of mapped gene data
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
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. Evaluate bias
is_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_biased,
    df=linked_data,
    note="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)