p3/preprocess/Asthma/code/GSE270312.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE270312"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE270312"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE270312.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE270312.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE270312.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")
# Data type conversion functions
def convert_trait(value: str) -> Optional[int]:
    if not value or ':' not in value:
        return None
    asthma_status = value.split(':')[1].strip().lower()
    if 'yes' in asthma_status:
        return 1
    elif 'no' in asthma_status:
        return 0
    return None

def convert_gender(value: str) -> Optional[int]:
    if not value or ':' not in value:
        return None
    gender = value.split(':')[1].strip().lower()
    if 'female' in gender:
        return 0
    elif 'male' in gender:
        return 1
    return None

# Gene expression data availability
is_gene_available = True  # RNA transcriptome data available

# Variable row identification
trait_row = 3  # 'asthma status'
gender_row = 2  # 'gender'
age_row = None  # Age not available in characteristics

# Initial validation and save metadata
is_trait_available = trait_row is not None
is_usable = 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
)

# Clinical feature extraction since trait_row is not None
selected_clinical = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview and save clinical data
preview_result = preview_df(selected_clinical)
print("Preview of clinical data:", preview_result)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.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 gene IDs appear to be valid human gene symbols like ABCF1, ACE, ACKR2, ACKR3, etc.
# The IDs match with official HGNC gene symbols, so no mapping is needed
requires_gene_mapping = False
# 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)