output/preprocess/Depression/code/GSE135524.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE135524"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE135524"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE135524.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE135524.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE135524.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
# Step 1: Determine gene expression availability
is_gene_available = True  # Based on series title and design, this is a gene expression dataset (whole blood)

# Step 2: Variable availability and converters
# From the sample characteristics:
# 0: individual, 1: age, 2: Sex, 3: bmi, 4: race, 5: HAMD score (severity), 6: college, 7: psychomotor score, 8: tissue
# No diagnosis/control field; background indicates all are depressed → trait (Depression) is constant → not available
trait_row = None
age_row = 1
gender_row = 2

def _after_colon(value: str) -> str:
    try:
        return value.split(":", 1)[1].strip()
    except Exception:
        return value

def convert_trait(x):
    # Generic heuristic if ever used: map depression/MDD/case to 1, control/healthy to 0; otherwise None
    v = _after_colon(str(x)).lower()
    if any(k in v for k in ["control", "healthy", "hc", "non-depressed", "nondepressed"]):
        return 0
    if any(k in v for k in ["depress", "mdd", "case", "patient"]):
        return 1
    return None

def convert_age(x):
    v = _after_colon(str(x))
    try:
        age_val = float(v)
        # Basic sanity check for human ages
        if 0 < age_val < 120:
            return age_val
    except Exception:
        pass
    return None

def convert_gender(x):
    v = _after_colon(str(x)).strip().lower()
    # Map female→0, male→1
    if v in ["female", "f", "0"]:
        return 0
    if v in ["male", "m", "1"]:
        return 1
    return None

# Step 3: Save metadata with 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
)

# Step 4: Clinical feature extraction (skip because trait not available)
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
    )
    _prev = preview_df(selected_clinical_df)
    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
# Illumina probe IDs (e.g., 'ILMN_1343291') are not human gene symbols and require mapping.
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
# Identify appropriate columns in the annotation for probe IDs and gene symbols
probe_col = 'ID'       # Matches probe IDs like 'ILMN_1343291' in expression data
gene_symbol_col = 'Symbol'  # Contains gene symbols

# Build mapping dataframe (ID -> Gene)
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 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 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. Link and downstream processing only if clinical trait data is available
linked_data = None
trait_available = ('selected_clinical_data' in locals()) and (trait in getattr(selected_clinical_data, 'index', []))

if trait_available:
    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

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

    # Final 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: Clinical features extracted; proceeded with linking and QC."
    )

    # 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: record metadata and do not attempt linking
    _ = 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,  # Ignored since trait is unavailable
        df=normalized_gene_data.T,  # Non-empty df for validation
        note="INFO: Trait not available (all subjects depressed); skipped linking and downstream analysis."
    )