output/preprocess/Bipolar_disorder/code/GSE46416.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE46416"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46416"

# Output paths
out_data_file = "./output/z1/preprocess/Bipolar_disorder/GSE46416.csv"
out_gene_data_file = "./output/z1/preprocess/Bipolar_disorder/gene_data/GSE46416.csv"
out_clinical_data_file = "./output/z1/preprocess/Bipolar_disorder/clinical_data/GSE46416.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

# 1) Gene expression data availability
is_gene_available = True  # Series describes gene expression profiling in blood; not miRNA-only or methylation.

# 2) Variable availability and conversion functions

# From the provided Sample Characteristics Dictionary:
# 0: tissue
# 1: disease status: bipolar disorder (BD) / control
# 2: bd phase: mania / euthymia
# 3: patient identifier
trait_row = 1
age_row = None
gender_row = None

def _after_colon(x):
    if x is None:
        return None
    s = str(x).strip().strip('"').strip()
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip()

def convert_trait(x):
    v = _after_colon(x)
    if v is None or v == '':
        return None
    vl = v.lower()
    # Map bipolar disorder cases to 1, controls to 0
    if 'control' in vl:
        return 0
    # capture terms indicating bipolar disorder
    if 'bipolar' in vl or re.search(r'\bbd\b', vl):
        return 1
    return None

def convert_age(x):
    # Not available for this cohort; keep here for completeness
    v = _after_colon(x)
    if not v:
        return None
    # extract first float/int number as age in years
    m = re.search(r'(\d+(\.\d+)?)', v)
    return float(m.group(1)) if m else None

def convert_gender(x):
    # Not available for this cohort; keep here for completeness
    v = _after_colon(x)
    if not v:
        return None
    vl = v.lower()
    if vl in ['male', 'm']:
        return 1
    if vl in ['female', 'f']:
        return 0
    # handle common encodings
    if 'male' in vl:
        return 1
    if 'female' in vl:
        return 0
    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 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=None,
        gender_row=gender_row,
        convert_gender=None
    )
    clinical_selected_preview = preview_df(selected_clinical_df)
    print(clinical_selected_preview)

    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
# Based on the provided probe-like numeric IDs (e.g., '2315252'), these are 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 columns for mapping based on annotation preview:
# Probe/ID column: 'ID' matches gene_data index (e.g., '2315252')
# Gene symbol column: 'gene_symbol'
probe_col = 'ID'
gene_col = 'gene_symbol'

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

# Apply mapping to convert probe-level data to gene-level data
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
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. Determine bias; guard against empty dataframe after missing-value handling
if linked_data.shape[0] == 0:
    is_trait_biased = True
    unbiased_linked_data = linked_data
else:
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final quality validation and save cohort info
# Ensure Python-native bools for JSON serialization
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 selected_clinical_df.index) and bool(selected_clinical_df.loc[trait].notna().any()))

# If the linked data is empty after processing, mark availability flags as False
if unbiased_linked_data.shape[0] == 0:
    is_gene_available_final = False
    # trait info might still exist in clinical, but dataset is unusable for analysis without samples
    # Keep trait flag as is to record availability; validate_and_save_cohort_info will also re-check
is_trait_biased = bool(is_trait_biased)

is_usable = 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,
    df=unbiased_linked_data,
    note="INFO: Gene-level mapping may be sparse; platform annotation had limited gene_symbol entries."
)

# 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)