p3/preprocess/Asthma/code/GSE230164.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE230164"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE230164"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE230164.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE230164.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE230164.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
# Since "Gene expression profiling" is mentioned in series title, 
# and series summary indicates this is a SuperSeries containing SubSeries,
# this dataset is likely to contain gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Based on sample characteristics, we can find:
# - Gender is available at key 0
# - Trait and age information are not explicitly available
trait_row = None 
age_row = None
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None # Not available

def convert_age(x):
    return None # Not available

def convert_gender(x):
    # Extract value after colon and convert to binary
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Save metadata about data availability
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. Since trait_row is None, skip clinical feature extraction
# 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 start with ILMN_ which indicates they are Illumina probe IDs
# These need to be mapped to human gene symbols for standardization
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. Based on the preview, 'ID' in annotation matches probe IDs in expression data (ILMN_*),
# and 'Symbol' contains gene symbols

# 2. Extract ID and Symbol columns to create mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Apply gene mapping to convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Create a minimal DataFrame for validation
linked_data = gene_data.T  # Transpose to have samples as rows

# 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=False,
    is_biased=True,  # No trait data means it's biased by definition
    df=linked_data,
    note="Dataset contains gene expression data but lacks required trait information."
)