p1/preprocess/Atherosclerosis/code/GSE90074.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE90074"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE90074"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE90074.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE90074.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE90074.csv"
json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"

# STEP 1: Initial Data Loading

# 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,
    prefixes_a=background_prefixes,
    prefixes_b=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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True

# 2.1 Data Availability
trait_row = None
age_row = None
gender_row = None

# 2.2 Data Type Conversions
def convert_trait(value: str):
    if not value:
        return None
    val = value.split(":", 1)[-1].strip().lower()
    # Example conversion logic:
    if "atherosclerosis" in val:
        return 1
    elif "control" in val:
        return 0
    return None

def convert_age(value: str):
    if not value:
        return None
    val = value.split(":", 1)[-1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str):
    if not value:
        return None
    val = value.split(":", 1)[-1].strip().lower()
    if val == "male":
        return 1
    elif val == "female":
        return 0
    return None

# 3. Save Metadata
is_trait_available = (trait_row is not None)
is_usable = 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
# Skip this step because trait_row is None
# 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])
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))
# STEP: Gene Identifier Mapping

# 1. Based on the preview of gene_annotation and the row IDs in gene_data, 
#    we identify 'ID' as the probe identifier column and 'GENE_SYMBOL' as the gene symbol column.

# 2. Obtain gene mapping from the annotation dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# (Optional) Print a summary of the resulting gene_data for verification
print("Resulting gene_data shape:", gene_data.shape)
print("Resulting gene_data head:\n", gene_data.head())
import os
import pandas as pd

# STEP 7

# 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)

# Check whether we actually have a clinical data file (which implies trait data was extracted).
if os.path.exists(out_clinical_data_file):
    # 2. Link the clinical and genetic data on sample IDs.
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0, index_col=0)
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

    # 3. Handle missing values in the linked data.
    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, and 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 is severely biased. No final data saved.")

else:
    # Trait data was never extracted, so record it as unavailable with is_final=False
    is_usable = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        note="No clinical data available; trait data missing."
    )
    print("No clinical data file found. Skipping linking and final data save.")