output/preprocess/Asthma/code/GSE205151.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE205151"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE205151.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE205151.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE205151.csv"
json_path = "./output/z1/preprocess/Asthma/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
import re

# 1) Gene expression availability (Nanostring mRNA -> gene expression data present)
is_gene_available = True

# 2) Variable availability
# All participants are children with asthma; no per-sample asthma status, age, or gender fields in the sample characteristics.
trait_row = None
age_row = None
gender_row = None

# 2.2) Conversion functions (defined but not used since corresponding rows are None)

def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(x):
    v = _after_colon(x)
    if v is None:
        return None
    vlow = v.lower()
    # Map asthma-related labels: case=1, control/healthy=0
    positives = ['asthma', 'status asthmaticus', 'severe asthma', 'critical asthma', 'case', 'patient']
    negatives = ['control', 'healthy', 'non-asthma', 'no asthma']
    if any(p in vlow for p in positives):
        return 1
    if any(n in vlow for n in negatives):
        return 0
    return None

def convert_age(x):
    v = _after_colon(x)
    if v is None:
        return None
    # Extract first floating number as age (in years)
    m = re.search(r'(\d+(?:\.\d+)?)', v)
    if m:
        try:
            return float(m.group(1))
        except:
            return None
    return None

def convert_gender(x):
    v = _after_colon(x)
    if v is None:
        return None
    vlow = v.lower().strip()
    if vlow in ['male', 'm', 'man', 'boy', '1']:
        return 1
    if vlow in ['female', 'f', 'woman', 'girl', '0']:
        return 0
    return None

# 3) Save metadata (initial filtering)
is_trait_available = trait_row is not None
_ = 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
)

# 4) Clinical feature extraction (skip because trait_row is None)
if trait_row is not None:
    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_df(selected_clinical_df)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
requires_gene_mapping = False
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save normalized gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Prepare defaults
linked_data = None

# 2–6. Proceed only if clinical features were extracted previously (i.e., trait data available)
tr = globals().get('trait_row', None)
scd = globals().get('selected_clinical_data', None)

if (tr is not None) and (scd is not None):
    # 2. Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(scd, normalized_gene_data)

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

    # 4. Determine bias and remove biased demographic features
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final validation 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=unbiased_linked_data,
        note="INFO: Finalized with available trait; demographic biases removed if present."
    )

    # 6. Save usable linked data
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        unbiased_linked_data.to_csv(out_data_file)
else:
    # Trait data is not available per sample; record metadata so the cohort is marked unusable for association.
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )