p1/preprocess/Arrhythmia/code/GSE55231.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE55231"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE55231"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE55231.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE55231.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE55231.csv"
json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Determine if gene expression data is available
is_gene_available = True  # Based on study description (eQTL analysis, transcription profiling)

# 2. Identify variable availability
# Trait "Arrhythmia" is not listed in the sample characteristics, so treat it as not available.
trait_row = None

# Age is provided under key 2
age_row = 2

# Gender is provided under key 0
gender_row = 0

# 2.2 Define conversion functions
def convert_trait(value: str):
    # Trait data is not available. Return None for all inputs.
    return None

def convert_age(value: str):
    # Parse the string after colon and convert to float if possible
    parts = value.split(':', 1)
    raw = parts[1].strip() if len(parts) > 1 else parts[0].strip()
    try:
        return float(raw)
    except ValueError:
        return None

def convert_gender(value: str):
    # Parse the string after colon and convert to binary (female=0, male=1)
    parts = value.split(':', 1)
    raw = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
    if raw == 'female':
        return 0
    elif raw == 'male':
        return 1
    return None

# 3. Initial usability filtering and metadata saving
is_trait_available = (trait_row is not None)
cohort_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])
# Based on observation, the "ILMN_" prefix indicates Illumina probe IDs, not standard human gene symbols.
# Therefore, they require mapping to gene symbols.
print("These identifiers are Illumina probe IDs.\nrequires_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 gene_annotation that match the probe ID and gene symbol
probe_col = 'ID'
gene_symbol_col = 'Symbol'

# 2. Create the gene mapping dataframe
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Just for a brief preview, let's check the resulting shape
print("Mapped gene_data shape:", gene_data.shape)
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Check if we have a clinical dataframe called 'selected_clinical_df' (which only exists if trait_row was not None)
if 'selected_clinical_df' in globals():
    # We have trait data, so we can link and proceed with the final steps.
    selected_clinical = selected_clinical_df

    # 3. Link the clinical and genetic data on sample IDs
    linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

    # 4. Handle missing values, removing or imputing as instructed
    linked_data = handle_missing_values(linked_data, trait)

    # 5. Determine whether the trait is severely biased.
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 6. Conduct final quality validation and save metadata
    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=linked_data,
        note="Cohort data successfully processed with trait-based analysis."
    )

    # 7. If the dataset is usable, save the final linked data
    if is_usable:
        linked_data.to_csv(out_data_file, index=True)
        print(f"Saved final linked data to {out_data_file}")
    else:
        print("The dataset is not usable for trait-based association. Skipping final output.")

else:
    # Trait data was not extracted in Step 2 (trait_row was None), so no clinical linking or bias checks.
    print("No trait data found. Skipping linking, missing value handling, and trait bias analysis.")
    # Perform an initial metadata save (not final) since we lack a trait.
    is_usable = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )
    # Without trait data, this dataset won't move forward to final association analysis
    print("No final output generated due to missing trait data.")