p1/preprocess/Endometriosis/code/GSE120103.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE120103"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE120103"

# Output paths
out_data_file = "./output/preprocess/1/Endometriosis/GSE120103.csv"
out_gene_data_file = "./output/preprocess/1/Endometriosis/gene_data/GSE120103.csv"
out_clinical_data_file = "./output/preprocess/1/Endometriosis/clinical_data/GSE120103.csv"
json_path = "./output/preprocess/1/Endometriosis/cohort_info.json"

# STEP1
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)
# 1. Gene Expression Data Availability
is_gene_available = True  # Identified "whole genome expression" in the dataset description.

# 2. Variable Availability
#    trait_row = 1, age_row = None, gender_row = None based on the sample characteristics dictionary.
trait_row = 1
age_row = None
gender_row = None

# 2.2 Data Type Conversion
def convert_trait(x: str):
    """Convert the sample group information to a binary trait variable: 0 for Disease free, 1 for Stage IV Ovarian Endometriosis."""
    if ':' in x:
        _, val = x.split(':', 1)
        val = val.strip().lower()
    else:
        val = x.strip().lower()
    if 'stage iv ovarian endometriosis' in val:
        return 1
    elif 'disease free endometrium' in val:
        return 0
    return None  # For any unexpected value

def convert_age(x: str):
    """No age data is available in this dataset."""
    return None

def convert_gender(x: str):
    """No gender variation in this dataset (all female)."""
    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_row is not 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
    )
    # Preview the extracted clinical features
    preview = preview_df(selected_clinical_df)
    print(preview)

    # Save the extracted clinical features
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 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])
# Based on the provided identifiers, they appear to be array probe IDs rather than standard human gene symbols.
# Therefore, gene mapping is required.

print("requires_gene_mapping = True")
# STEP5
# 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))
# 1. Identify the matching columns in the gene annotation for the probe IDs and gene symbols.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")

# 2. Convert probe-level measurements to gene-level expression data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print a quick check of the resulting gene_data.
print("Mapped gene_data shape:", gene_data.shape)
print("First 5 mapped gene symbols:", gene_data.index[:5].tolist())
# STEP 7

import pandas as pd

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
trait_biased, df = judge_and_remove_biased_features(df, trait)

# 5. Perform final validation with full dataset information.
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=trait_biased,
    df=df,
    note="Final step with linking, missing-value handling, bias checks."
)

# 6. If the data is usable, save the final linked data.
if is_usable:
    df.to_csv(out_data_file)
    print(f"Final linked data saved to: {out_data_file}")
else:
    print("Dataset is not usable or severely biased. No final data saved.")