output/preprocess/Depression/code/GSE110298.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE110298"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE110298"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE110298.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE110298.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE110298.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 math
import pandas as pd

# 1) Gene expression data availability (based on series summary: hippocampal gene expression microarrays)
is_gene_available = True

# 2) Identify rows in the Sample Characteristics Dictionary
trait_row = 6    # 'depression: ...'
age_row = 2      # 'age: ...'
gender_row = 1   # 'sex (self-reported): ...'

# 2.2) Converters
def _after_colon(x: str) -> str:
    if x is None or (isinstance(x, float) and math.isnan(x)):
        return ''
    # Use the last segment after colon to handle fields with multiple colons in other keys
    parts = str(x).split(':')
    return parts[-1].strip() if len(parts) >= 2 else str(x).strip()

def _to_number(val: str):
    try:
        if val == '' or val.lower() in {'na', 'n/a', 'nan', 'none', 'null', 'missing', '.'}:
            return None
        # Prefer int if it looks like int
        f = float(val)
        if f.is_integer():
            return int(f)
        return f
    except Exception:
        return None

# Trait is continuous (depressive symptom count/score)
def convert_trait(x):
    val = _after_colon(x)
    return _to_number(val)

# Age is continuous
def convert_age(x):
    val = _after_colon(x)
    return _to_number(val)

# Gender is binary: female -> 0, male -> 1
def convert_gender(x):
    val = _after_colon(x).lower()
    if val in {'female', 'f', 'woman', 'women'}:
        return 0
    if val in {'male', 'm', 'man', 'men'}:
        return 1
    return None

# 3) Save initial 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
)

# 4) Clinical Feature Extraction (only if trait_row 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
    )
    # Preview and save
    preview = preview_df(selected_clinical_df, n=5)
    print("Selected clinical features preview:", 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
# Affymetrix probe set IDs detected (e.g., '1007_s_at'); mapping to gene symbols is required
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
# Ensure required dataframes are available from previous steps
try:
    gene_annotation
except NameError:
    gene_annotation = get_gene_annotation(soft_file)

try:
    gene_data
except NameError:
    gene_data = get_genetic_data(matrix_file)

# 1-2) Build mapping from probe IDs to gene symbols
# Probe ID column: 'ID'; Gene symbol column: 'Gene Symbol'
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3) Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping_df)

# Step 7: Data Normalization and Linking
import os
import pandas as pd

# 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
# Use the correctly named clinical dataframe; if missing (e.g., new session), load from file
if 'selected_clinical_df' not in globals():
    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 checks (remove biased covariates if needed)
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
# Ensure pure Python bools for JSON serialization
is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
trait_col_present = bool(trait in linked_data.columns)
has_any_trait = bool(linked_data[trait].notna().any()) if trait_col_present else False
is_trait_available_final = bool(trait_col_present and has_any_trait)
is_trait_biased = bool(is_trait_biased)

note = ("INFO: Affymetrix probe sets mapped to gene symbols; hippocampal tissue; "
        "Depression treated as continuous symptom count; Age and Gender included; "
        "standard missingness filtering and imputation applied.")
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=note
)

# 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)