output/preprocess/Bipolar_disorder/code/GSE62191.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE62191"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE62191"

# Output paths
out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE62191.csv"
out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE62191.csv"
out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE62191.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
import os
import re
import pandas as pd

# 1) Gene expression availability
is_gene_available = True  # Based on series title/summary indicating mRNA gene expression profiling

# 2) Variable availability and converters
# From Sample Characteristics:
# - trait_row: 1 ('disease state: healthy control' | 'bipolar disorder' | 'schizophrenia')
# - age_row: 2 ('age: XX yr')
# - gender_row: None (only 'gender: male' observed; constant feature -> not useful)
trait_row = 1
age_row = 2
gender_row = None

def convert_trait(x):
    # Map bipolar disorder to 1; treat schizophrenia and healthy controls as non-BD (0)
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return None
    val = str(x)
    if ':' in val:
        val = val.split(':', 1)[1]
    v = val.strip().lower()
    if 'bipolar' in v:
        return 1
    if ('healthy' in v) or ('control' in v):
        return 0
    if 'schizo' in v:
        return 0
    return None

def convert_age(x):
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return None
    val = str(x)
    if ':' in val:
        val = val.split(':', 1)[1]
    m = re.search(r'(\d+(\.\d+)?)', val)
    if m:
        try:
            num = float(m.group(1))
            return num
        except Exception:
            return None
    return None

def convert_gender(x):
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return None
    val = str(x)
    if ':' in val:
        val = val.split(':', 1)[1]
    v = val.strip().lower()
    if 'female' in v:
        return 0
    if 'male' in v:
        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=convert_gender
    )
    clinical_preview = preview_df(selected_clinical_df)
    print(clinical_preview)
    print(f"Selected clinical features shape: {selected_clinical_df.shape}")

    # Save clinical data
    out_dir = os.path.dirname(out_clinical_data_file)
    os.makedirs(out_dir, 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
# 1-2) Decide columns and create mapping dataframe
prob_col = 'ID'           # Matches probe IDs in gene_data (numeric strings like '12', '13', ...)
gene_col = 'GENE_SYMBOL'  # Stores human gene symbols

mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_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
from json import JSONDecodeError

# 1. Normalize gene symbols and save gene 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. Ensure clinical data is available in this step: reload if needed
try:
    selected_clinical_df
except NameError:
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

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 assessment and removal of biased demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final quality validation and save cohort info
is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
is_trait_available_final = bool((trait in linked_data.columns) and (linked_data[trait].notna().any()))
is_trait_biased_bool = bool(is_trait_biased)

note = "INFO: Age available; Gender not available in clinical annotations for this series."

def run_validate():
    return validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=is_gene_available_final,
        is_trait_available=is_trait_available_final,
        is_biased=is_trait_biased_bool,
        df=unbiased_linked_data,
        note=note
    )

try:
    is_usable = run_validate()
except (TypeError, JSONDecodeError):
    # Repair/reset JSON file and retry
    os.makedirs(os.path.dirname(json_path), exist_ok=True)
    with open(json_path, "w") as f:
        f.write("{}")
    is_usable = run_validate()

# 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)