output/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/z1/preprocess/Asthma/GSE185658.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE185658.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
import os
from typing import Any

# 1. Gene Expression Data Availability
is_gene_available = True  # Affymetrix microarray gene expression per background info

# 2. Variable Availability and Data Type Conversion

# Based on the sample characteristics:
# {0: ['time: DAY14', 'time: DAY4'],
#  1: ['group: AsthmaHDM', 'group: Healthy', 'group: AsthmaHDMNeg'],
#  2: ['donor: DJ...']}
trait_row = 1  # 'group' field indicates asthma status (patients vs healthy controls)
age_row = None
gender_row = None

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

def convert_trait(value):
    v = _after_colon(value).strip().lower()
    # normalize to alphanumerics only to catch variants like "asthma-hdm", "asthma_hdm"
    v_norm = re.sub(r"[^a-z0-9]+", "", v)

    if v_norm in {"healthy", "control", "ctrl"}:
        return 0
    # Treat both AsthmaHDM and AsthmaHDMNeg as asthma cases
    if v_norm in {"asthma", "asthmahdm", "asthmahdmneg"}:
        return 1
    # Fallback heuristics
    if "asthma" in v_norm:
        return 1
    if "healthy" in v_norm or "control" in v_norm or v_norm == "ctrl":
        return 0
    return None

def convert_age(value):
    v = _after_colon(value).lower()
    nums = re.findall(r"[0-9]+(?:\.[0-9]+)?", v)
    if not nums:
        return None
    try:
        age = float(nums[0])
        if age <= 0 or age > 120:
            return None
        return age
    except Exception:
        return None

def convert_gender(value):
    v = _after_colon(value).strip().lower()
    if v in {"female", "f", "woman", "girl"}:
        return 0
    if v in {"male", "m", "man", "boy"}:
        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 clinical data 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
    )
    preview = preview_df(selected_clinical_df)
    print(preview)
    # Save
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# 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
# Determine the appropriate columns for mapping: 'ID' (probe IDs) and 'gene_assignment' (contains gene symbols)
prob_col = 'ID'
gene_col = 'gene_assignment'

# 1-2. Build the mapping dataframe from the annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)

# 3. Apply the mapping to convert probe-level data to gene-level data
probe_data = gene_data  # keep original probe-level data
gene_data = apply_gene_mapping(probe_data, 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 drop biased covariates if necessary
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final quality validation and save cohort info
note = ("INFO: Trait inferred from 'group' field; Age/Gender not available. "
        "Affymetrix probe data mapped via 'gene_assignment' and normalized with NCBI synonyms.")
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 only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)