output/preprocess/Asthma/code/GSE230164.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE230164"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE230164"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE230164.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE230164.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE230164.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
# Determine gene expression availability
is_gene_available = True  # Title indicates gene expression profiling; suitable (not miRNA/methylation)

# Identify variable availability from the provided sample characteristics dictionary
trait_row = None  # No sample-level trait (Asthma) indicator available; likely constant within a SuperSeries
age_row = None    # No age information present
gender_row = 0    # Gender available at key 0 with varying values

# Define conversion functions
def _after_colon(value: str) -> str:
    if value is None:
        return ""
    parts = str(value).split(":", 1)
    return parts[1].strip().lower() if len(parts) > 1 else str(value).strip().lower()

def convert_trait(x):
    v = _after_colon(x)
    # Generic asthma case/control mapping (not used here since trait_row is None)
    if v in {"asthma", "case", "patient", "disease", "asthmatic"}:
        return 1
    if v in {"control", "healthy", "normal", "non-asthma", "nonasthma", "non asthmatic"}:
        return 0
    return None

def convert_age(x):
    v = _after_colon(x)
    # Extract first numeric occurrence as age
    import re
    m = re.search(r"(\d+(\.\d+)?)", v)
    if not m:
        return None
    age = float(m.group(1))
    # Filter out implausible ages
    if 0 <= age <= 120:
        return age
    return None

def convert_gender(x):
    v = _after_colon(x)
    if v in {"male", "m", "man"}:
        return 1
    if v in {"female", "f", "woman"}:
        return 0
    return None

# Initial filtering and save metadata
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
)

# Clinical feature extraction (skip if 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 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)
    # Save clinical data
    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
# Determine if gene identifiers require mapping to human gene symbols based on known Illumina probe ID pattern
probe_ids = ['ILMN_1343291', 'ILMN_1343295', 'ILMN_1651199', 'ILMN_1651209',
             'ILMN_1651210', 'ILMN_1651221', 'ILMN_1651228', 'ILMN_1651229',
             'ILMN_1651230', 'ILMN_1651232', 'ILMN_1651235', 'ILMN_1651236',
             'ILMN_1651237', 'ILMN_1651238', 'ILMN_1651249', 'ILMN_1651253',
             'ILMN_1651254', 'ILMN_1651259', 'ILMN_1651260', 'ILMN_1651262']

requires_gene_mapping = any(x.startswith('ILMN_') for x in probe_ids)

print(f"requires_gene_mapping = {requires_gene_mapping}")

# 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 appropriate columns for probe IDs and gene symbols from the gene annotation dataframe
probe_col = 'ID' if 'ID' in gene_annotation.columns else None

# Prefer 'Symbol' for gene symbols; if not usable, fall back to 'ILMN_Gene'
if 'Symbol' in gene_annotation.columns and not gene_annotation['Symbol'].isna().all():
    gene_symbol_col = 'Symbol'
elif 'ILMN_Gene' in gene_annotation.columns and not gene_annotation['ILMN_Gene'].isna().all():
    gene_symbol_col = 'ILMN_Gene'
else:
    # If neither is available, raise an error to surface the issue
    raise ValueError("No suitable gene symbol column found in annotation (checked 'Symbol' and 'ILMN_Gene').")

if probe_col is None:
    raise ValueError("No suitable probe ID column found in annotation (expected 'ID').")

# 2. Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 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 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)

# Determine trait availability from prior steps; fall back to presence of selected_clinical_df
if 'is_trait_available' in globals():
    trait_available = bool(is_trait_available)
else:
    trait_available = 'selected_clinical_df' in globals()

is_gene_available = True

# 2-6. Proceed only if clinical/trait data is available; otherwise, record metadata and skip linking/QC
if trait_available and ('selected_clinical_df' in globals()):
    # 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. Bias checks 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 metadata
    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=True,
        is_biased=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Clinical features linked and QC performed."
    )

    # 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 record metadata appropriately
    linked_data = None
    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=False,
        is_biased=False,
        df=normalized_gene_data.T,  # Use sample x features shape for sanity checks
        note=f"WARNING: Trait ({trait}) not available at sample level; only gene matrix saved."
    )