output/preprocess/Asthma/code/GSE188424.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE188424"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE188424"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE188424.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE188424.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE188424.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

# 1) Gene expression data availability (from background info: gene expression profiling on human whole blood)
is_gene_available = True

# 2) Variable availability based on the provided Sample Characteristics Dictionary:
# Only gender is available under key 0; trait (controlled vs uncontrolled asthma) and age are not explicitly available.
trait_row = None
age_row = None
gender_row = 0

# 2.2) Data type conversion functions
def _after_colon(value):
    if value is None:
        return None
    s = str(value)
    parts = s.split(":", 1)
    v = parts[1] if len(parts) > 1 else parts[0]
    return v.strip()

def convert_trait(value):
    # Not available in this dataset; return None safely in case of accidental use.
    v = _after_colon(value)
    if v is None:
        return None
    vl = v.lower()
    # Heuristic mapping if ever present:
    # Map controlled (0) vs uncontrolled (1)
    if "uncontrolled" in vl:
        return 1
    if "controlled" in vl:
        return 0
    # If some free-text indicating asthma/no asthma (unlikely in this controlled/uncontrolled study)
    if vl in {"asthma", "case", "patient"}:
        return 1
    if vl in {"control", "healthy", "non-asthma", "no asthma"}:
        return 0
    return None

def convert_age(value):
    # Not available in this dataset; robust parser provided for completeness.
    v = _after_colon(value)
    if v is None:
        return None
    m = re.search(r'(\d+(\.\d+)?)', v)
    return float(m.group(1)) if m else None

def convert_gender(value):
    v = _after_colon(value)
    if v is None:
        return None
    vl = v.strip().lower()
    if vl in {"male", "m", "man", "boy"}:
        return 1
    if vl in {"female", "f", "woman", "girl"}:
        return 0
    if vl in {"unknown", "na", "n/a", "nan", ""}:
        return None
    return None

# 3) Save metadata with 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 were available, we would extract as below:
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 if age_row is not None else None,
        gender_row=gender_row,
        convert_gender=convert_gender if gender_row is not None else None
    )
    preview = preview_df(selected_clinical_df, n=5)
    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
# Identify the appropriate columns for probe IDs and gene symbols
probe_col = 'ID'
symbol_col = 'Symbol'
assert probe_col in gene_annotation.columns and symbol_col in gene_annotation.columns

# Build mapping dataframe (probe -> gene symbol)
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

# Apply mapping to convert probe-level expression 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 gene-level 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)

# Determine trait availability from earlier step
is_gene_available = True
is_trait_available = (locals().get('trait_row', None) is not None)

if is_trait_available:
    # Ensure clinical features are available; if not, extract them now
    if 'selected_clinical_data' not in globals():
        selected_clinical_data = 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 if age_row is not None else None,
            gender_row=gender_row,
            convert_gender=convert_gender if gender_row is not None else None
        )

    # 2) Link the clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

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

    # 4) Bias checks
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5) Final validation and save cohort info
    note = "INFO: Clinical trait available; proceeded with linking and QC."
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=is_gene_available,
        is_trait_available=is_trait_available,
        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)

else:
    # Trait not available; skip linking and downstream steps
    note = "WARNING: Trait data not available (trait_row is None). Recorded dataset as unavailable for association analysis."
    # Provide a non-empty df with sufficient columns to avoid abnormality override in validation
    dummy_df = normalized_gene_data.T
    _ = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=is_gene_available,
        is_trait_available=False,
        is_biased=False,  # placeholder; not used when trait unavailable
        df=dummy_df,
        note=note
    )