output/preprocess/Duchenne_Muscular_Dystrophy/code/GSE79263.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"
cohort = "GSE79263"

# Input paths
in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE79263"

# Output paths
out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE79263.csv"
out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE79263.csv"
out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE79263.csv"
json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/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 availability
is_gene_available = True  # Transcriptomic gene expression per background info

# 2) Variable availability
trait_row = 2  # 'disease state' with values like Duchenne muscular dystrophy / healthy
age_row = 4    # 'age' with numeric years or unknown
gender_row = None  # No gender information found

# 2.2) Converters
def _after_colon(value: str) -> str:
    if value is None:
        return ""
    s = str(value).strip()
    if ":" in s:
        s = s.split(":", 1)[1].strip()
    return s

def convert_trait(value):
    v = _after_colon(value).strip().lower()
    if v in {"na", "n/a", "unknown", ""}:
        return None
    # Map DMD cases to 1
    if ("duchenne" in v) or (v == "dmd") or ("duchenne muscular dystropy" in v):
        return 1
    # Map healthy/control to 0
    if v in {"healthy", "control", "normal"}:
        return 0
    return None

def convert_age(value):
    v = _after_colon(value).strip().lower()
    if v in {"na", "n/a", "unknown", ""}:
        return None
    m = re.search(r"(\d+(\.\d+)?)", v)
    if m:
        try:
            # Return as float if decimals exist, else int
            num = float(m.group(1))
            return int(num) if num.is_integer() else num
        except Exception:
            return None
    return None

def convert_gender(value):
    v = _after_colon(value).strip().lower()
    if v in {"female", "f"}:
        return 0
    if v in {"male", "m"}:
        return 1
    if v in {"na", "n/a", "unknown", ""}:
        return None
    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 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=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    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
# Select appropriate columns for mapping: probe IDs ('ID') and gene symbols ('Symbol')
prob_col = 'ID'
gene_col = 'Symbol'

# Build mapping dataframe from annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_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)

# Normalize gene symbols to standard symbols and aggregate duplicates
gene_data = normalize_gene_symbols_in_index(gene_data)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save normalized gene expression data
# Note: gene_data was already normalized in Step 6; calling again is idempotent and safe.
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
is_usable = validate_and_save_cohort_info(
    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
)

# 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)