output/preprocess/Depression/code/GSE99725.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE99725"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE99725"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE99725.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE99725.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE99725.csv"
json_path = "./output/z2/preprocess/Depression/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 (whole-genome expression profiling from peripheral blood)
is_gene_available = True

# 2) Variable availability and conversion functions
# From the sample characteristics dictionary:
# 0: patient IDs (not useful for analysis)
# 1: time: M0 / M6 (time point)
# 2: MADRS: A / B (use as proxy for Depression status)
# 3: tissue: Venous blood (constant)
trait_row = 2
age_row = None
gender_row = None

def _extract_value_after_colon(x):
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip()

def convert_trait(x):
    """
    Convert MADRS grouping or depression-related labels to binary:
    - Map 'A' (group A) -> 1, 'B' (group B) -> 0
    - Also handle common synonyms if present.
    """
    v = _extract_value_after_colon(x)
    if v is None:
        return None
    lv = v.strip().lower()

    # Direct group labels
    if lv in {'a', 'group a'}:
        return 1
    if lv in {'b', 'group b'}:
        return 0

    # Common semantic fallbacks if present
    if lv in {'depressed', 'depression', 'mdd', 'case', 'patient', 'baseline', 'm0'}:
        return 1
    if lv in {'remitted', 'non-depressed', 'control', 'healthy', 'post-op', 'postoperative', 'm6'}:
        return 0

    # If numeric MADRS score was provided, classify using a common clinical threshold
    # (>=7 often indicates at least mild depression)
    try:
        score = float(re.findall(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', lv)[0])
        return 1 if score >= 7 else 0
    except Exception:
        return None

def convert_age(x):
    v = _extract_value_after_colon(x)
    if v is None:
        return None
    nums = re.findall(r'\d+\.?\d*', v)
    if not nums:
        return None
    try:
        return float(nums[0])
    except Exception:
        return None

def convert_gender(x):
    v = _extract_value_after_colon(x)
    if v is None:
        return None
    lv = v.strip().lower()
    if lv in {'female', 'f', 'woman', 'women'}:
        return 0
    if lv in {'male', 'm', 'man', 'men'}:
        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=None,
        gender_row=gender_row,
        convert_gender=None
    )

    # Preview and save
    preview = preview_df(selected_clinical_df)
    print(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
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
# Determine appropriate columns for mapping: probe IDs and gene symbols
id_col = 'ID'
gene_symbol_col = 'GENE_SYMBOL'
assert id_col in gene_annotation.columns and gene_symbol_col in gene_annotation.columns

# 2) Build mapping dataframe (probe -> gene symbol)
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_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
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. Assess bias 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 info
note = "INFO: Trait derived from MADRS grouping (A=case, B=control) as proxy for Depression status."
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=note
)

# 6. Save linked dataset if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)