output/preprocess/Bipolar_disorder/code/GSE45484.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE45484"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE45484"

# Output paths
out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE45484.csv"
out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE45484.csv"
out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv"
json_path = "./output/z1/preprocess/Bipolar_disorder/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 data availability based on provided background and sample characteristics
is_gene_available = True  # Gene expression microarray from whole blood RNA
trait_row = None          # All subjects have bipolar disorder -> trait not variable here
age_row = 4               # 'age: <number>'
gender_row = 3            # 'sex: F'/'sex: M'

# Converters
def convert_trait(x):
    # Trait (Bipolar_disorder) is constant in this cohort; mark as unavailable
    return None

def _after_colon(val: str) -> str:
    if val is None:
        return ""
    s = str(val)
    return s.split(":", 1)[1].strip() if ":" in s else s.strip()

def convert_age(x):
    v = _after_colon(x)
    if v == "" or v.lower() in {"na", "n/a", "nan", "null", "unknown", "?", "none"}:
        return None
    try:
        return float(v)
    except Exception:
        return None

def convert_gender(x):
    v = _after_colon(x).strip().lower()
    if v in {"f", "female", "woman", "women"}:
        return 0
    if v in {"m", "male", "man", "men"}:
        return 1
    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: only proceed if trait is available (not in this dataset)
if is_trait_available:
    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
    )
    clinical_preview = preview_df(selected_clinical_df)
    print(clinical_preview)
    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
# ILMN_* identifiers are Illumina probe IDs, not human gene symbols
requires_gene_mapping = True
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
# Decide identifier and gene symbol columns based on annotation preview
id_col = 'ID'  # ILMN_* probe IDs
gene_col = 'Symbol' if 'Symbol' in gene_annotation.columns else (
    'ILMN_Gene' if 'ILMN_Gene' in gene_annotation.columns else None
)

if gene_col is None:
    raise ValueError("No suitable gene symbol column found in annotation (expected 'Symbol' or 'ILMN_Gene').")

# 2. Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_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
import pandas as pd

# 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)

# 2-6. Proceed only if clinical data with trait exists; otherwise perform final validation noting trait unavailability
has_selected_clinical = (
    ('selected_clinical_data' in globals()) and
    (selected_clinical_data is not None) and
    (not selected_clinical_data.empty) and
    (trait in selected_clinical_data.index)
)

if has_selected_clinical:
    # 2. Link 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 check and remove biased covariates
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final quality validation and metadata saving
    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: Linked clinical-genetic data generated from GEO series."
    )

    # 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 is unavailable in this cohort (all subjects have Bipolar_disorder); skip linking but record correct metadata
    print("Skipping linking and downstream steps: trait is not available/variable in this cohort.")
    df_for_validation = normalized_gene_data.T  # Non-empty placeholder to avoid abnormality override
    _ = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=False,
        df=df_for_validation,
        note="INFO: Trait not variable/recorded in this cohort (all subjects have Bipolar_disorder). Only gene data saved."
    )