output/preprocess/Asthma/code/GSE182797.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE182797"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE182797"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE182797.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE182797.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE182797.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 os
import re
import pandas as pd

# 1) Gene expression data availability
is_gene_available = True  # Microarray transcriptome profiling indicates gene expression data

# 2) Variable availability
trait_row = 0  # 'diagnosis' with multiple categories including asthma
age_row = 2    # 'age' values available and varying
gender_row = None  # Only 'Female' present => constant => not useful

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

def convert_trait(x):
    v = _after_colon(x)
    if v is None:
        return None
    v_low = str(v).strip().lower()
    # Map presence of asthma to 1, others (healthy, IEI) to 0
    if "asthma" in v_low:
        return 1
    if v_low in {"healthy", "control", "controls"}:
        return 0
    if v_low in {"iei", "idiopathic environmental intolerance"}:
        return 0
    return None

def convert_age(x):
    v = _after_colon(x)
    if v is None:
        return None
    v = str(v).strip().lower()
    if v in {"na", "n/a", "nan", "none", ""}:
        return None
    # Extract first float in the string
    m = re.search(r"-?\d+(\.\d+)?", v)
    if not m:
        return None
    try:
        return float(m.group(0))
    except Exception:
        return None

def convert_gender(x):
    v = _after_colon(x)
    if v is None:
        return None
    v_low = str(v).strip().lower()
    if v_low in {"female", "f"}:
        return 0
    if v_low in {"male", "m"}:
        return 1
    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 (only if trait is available)
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=None
    )
    preview = preview_df(selected_clinical_df)
    print(preview)
    # Save clinical data
    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
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# 1-2) Decide columns and build mapping dataframe
# Probe identifiers: 'ID'; Gene symbols: 'GENE_SYMBOL'
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# 3) Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save
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)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Assess bias and remove biased covariates
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "INFO: Gender not provided or constant (female only) per series description; excluded as covariate."
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=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)