p1/preprocess/Atherosclerosis/code/GSE154851.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE154851"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE154851"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE154851.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE154851.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE154851.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. Decide gene expression data availability
is_gene_available = True  # from background info: "Human Gene Expression 8x60K Microarray kit"

# 2. Identify availability rows for trait, age, and gender
trait_row = None  # No row indicates atherosclerosis status in the sample characteristics
age_row = 2       # Row 2 has multiple age values
gender_row = 1    # Row 1 has multiple gender values

# 2.2 Define conversion functions

def convert_trait(value: str):
    # No actual data available, return None
    return None

def convert_age(value: str):
    # Example: "age: 37y"
    try:
        # Split by colon, remove 'y', convert to float
        parts = value.split(':')
        if len(parts) < 2:
            return None
        age_str = parts[1].strip().lower().replace('y', '')
        return float(age_str)
    except:
        return None

def convert_gender(value: str):
    # Example: "gender: male" -> 1, "gender: female" -> 0
    try:
        parts = value.split(':')
        if len(parts) < 2:
            return None
        gender_str = parts[1].strip().lower()
        if 'female' in gender_str:
            return 0
        elif 'male' in gender_str:
            return 1
        else:
            return None
    except:
        return None

# 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. Since trait_row is None, skip clinical feature extraction
# 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 given IDs (1,2,3,...) do not match standard human gene symbol format,
# so they likely need to be mapped to proper gene symbols
print("These identifiers appear to be numeric, not standard human 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: Gene Identifier Mapping

# 1. Identify the columns in the annotation that match the gene expression data's IDs and the gene symbol
#    In this dataset, 'ID' corresponds to the probe identifiers, and 'GENE_SYMBOL' corresponds to the gene symbols.

# 2. Get a gene mapping dataframe by extracting these two columns
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# (Optional) Print some info to verify the result
print("Gene data shape after mapping:", gene_data.shape)
print(gene_data.head())
# STEP 7

import os
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)

# Check if a clinical data file was ever generated (which indicates trait data was available).
if not os.path.exists(out_clinical_data_file):
    print("No clinical data file found. This implies trait data is unavailable.")
    # In this scenario, we do a partial validation (is_final=False) because we cannot finalize without trait data.
    is_usable = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )
    print("Initial validation recorded for missing trait data. No final data will be saved.")
else:
    # 2. Link the clinical and genetic data on sample IDs
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    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 severely biased
    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 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.")