output/preprocess/Duchenne_Muscular_Dystrophy/code/GSE109178.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"
cohort = "GSE109178"

# Input paths
in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE109178"

# Output paths
out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE109178.csv"
out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE109178.csv"
out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE109178.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
# Step 1: Gene expression availability (Affymetrix HG-U133 Plus 2.0 mRNA arrays)
is_gene_available = True

# Step 2.1: Determine availability rows from the provided Sample Characteristics Dictionary
# Trait (Duchenne Muscular Dystrophy) label is not explicitly present -> cannot infer reliably from provided keys
trait_row = None

# Age is available at key 0
age_row = 0

# Gender is available at key 3
gender_row = 3

# Step 2.2: Conversion functions

def _after_colon(val: str) -> str:
    if val is None:
        return ""
    parts = str(val).split(":", 1)
    v = parts[1] if len(parts) > 1 else parts[0]
    return v.strip()

def convert_trait(val):
    # No trait field available; return None to indicate missing
    _ = _after_colon(val)
    return None

def convert_age(val):
    v = _after_colon(val).lower()
    if v in {"na", "n/a", "", "none"}:
        return None
    # remove potential units and commas
    v = v.replace("years", "").replace("year", "").replace("yrs", "").replace("yr", "").replace(",", "").strip()
    try:
        return float(v)
    except Exception:
        return None

def convert_gender(val):
    v = _after_colon(val).strip().lower()
    if v in {"m", "male"}:
        return 1
    if v in {"f", "female"}:
        return 0
    if v in {"na", "n/a", "", "none"}:
        return None
    # occasional typos or single letters
    if v.startswith("m"):
        return 1
    if v.startswith("f"):
        return 0
    return None

# Step 3: Initial filtering metadata save
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_row is None)
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
    )
    print(preview_df(selected_clinical_df))
    # Save clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# 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", "1552256_a_at"), not HGNC 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
# Identify the appropriate columns for mapping based on the annotation preview
probe_id_col = 'ID'
gene_symbol_col = 'Gene Symbol'

# Extract the mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_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-level 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. Attempt to link only if clinical data with trait is available
clinical = None
if 'selected_clinical_data' in globals():
    clinical = selected_clinical_data
elif 'selected_clinical_df' in globals():
    clinical = selected_clinical_df

has_trait_data = (clinical is not None) and (trait in getattr(clinical, 'index', []))

if has_trait_data:
    # 2. Link
    linked_data = geo_link_clinical_genetic_data(clinical, normalized_gene_data)

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

    # 4. Bias checks
    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(
        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: Trait labels available and data linked."
    )

    # 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, index=True)
else:
    # Trait not available: skip linking and final validation; ensure metadata reflects unavailability.
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )