p1/preprocess/Atherosclerosis/code/GSE87005.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE87005"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE87005"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE87005.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE87005.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE87005.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  # Based on the transcriptomic profiling info

# 2. Variable Availability
trait_row = None   # No column found that corresponds to "Atherosclerosis"
age_row = None     # No column found for age
gender_row = None  # No column found for gender

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[float]:
    parts = value.split(':', 1)
    val = parts[-1].strip() if len(parts) > 1 else value.strip()
    return None  # No valid trait data; returning None for all inputs

def convert_age(value: str) -> Optional[float]:
    parts = value.split(':', 1)
    val = parts[-1].strip() if len(parts) > 1 else value.strip()
    return None  # No valid age data; returning None for all inputs

def convert_gender(value: str) -> Optional[int]:
    parts = value.split(':', 1)
    val = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
    return None  # No valid gender data; returning None for all inputs

# 3. Save Metadata (initial filtering)
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 (trait data not available)
# 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])
# The gene identifiers appear to be microarray probe IDs rather than standard human gene symbols.
print("These appear to be microarray probe IDs that require mapping to gene symbols.")
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 6: Gene Identifier Mapping

# 1) From the annotation preview, we see the same kind of IDs are stored in column "ID",
#    and the gene symbols are in column "GENE_SYMBOL".
# 2) Get the mapping dataframe.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col='ID',
    gene_col='GENE_SYMBOL'
)

# 3) Convert probe-level measurements to gene-level data.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7

import pandas as pd
import os

# 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 if the clinical file actually exists
if not os.path.exists(out_clinical_data_file):
    # Trait data was not available, so no clinical file was ever written
    print("Clinical data file not found; trait data not available.")

    # Perform final validation indicating the trait is missing, and provide is_biased=False
    # plus an empty DataFrame to fulfill the function signature for final validation.
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=False,   # Must be a boolean, even though trait isn't available
        df=pd.DataFrame(), # Provide an empty DataFrame to finalize
        note="No trait data available to finish pipeline."
    )
    if not is_usable:
        print("No final data saved.")
    else:
        print("Data unexpectedly marked usable despite no trait data.")
else:
    # 2. Read the clinical data file and link with genetic data
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
    # If there's only one row, label its index with the trait name
    if len(selected_clinical_df) == 1:
        selected_clinical_df.index = [trait]

    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

    # 3. Handle missing values
    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
    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 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.")