p1/preprocess/Arrhythmia/code/GSE136992.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE136992"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE136992"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE136992.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE136992.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE136992.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 gene expression data availability
is_gene_available = True  # This dataset includes Illumina whole genome gene expression data

# 2.1 Determine data availability (keys for trait, age, gender)
trait_row = None  # No row found for "Arrhythmia" in the sample characteristics
age_row = 2       # Row 2 contains multiple distinct 'age' values
gender_row = 3    # Row 3 contains multiple distinct 'gender' values

# 2.2 Define data type conversions
def convert_trait(value: str):
    # Trait data is not available; return None
    return None

def convert_age(value: str):
    """Convert 'age: XX weeks' to a numeric type."""
    try:
        # Extract the substring after 'age:' and strip spaces
        raw = value.split(':', 1)[1].strip()
        # Remove the word 'weeks' if present
        raw = raw.lower().replace('weeks', '').strip()
        return float(raw)
    except:
        return None

def convert_gender(value: str):
    """Convert 'gender: male/female' to binary (0=female, 1=male)."""
    try:
        raw = value.split(':', 1)[1].strip().lower()
        if raw == 'male':
            return 1
        elif raw == 'female':
            return 0
        else:
            return None
    except:
        return None

# 3. Conduct initial filtering and save metadata
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. Skip clinical feature extraction, since 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])
# These identifiers, starting with "ILMN_", are Illumina probe IDs rather than standard gene symbols.
# Therefore, they need to be mapped to the corresponding 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. Decide which columns hold the probe IDs (same as gene_data.index) and the gene symbols.
#    From the annotation preview, "ID" matches the probe IDs, and "Symbol" contains the gene symbols.

# 2. Get a gene mapping dataframe (probe ID -> gene symbol).
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

# 3. Convert the probe-level expression data to gene-level data using the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

# First, check if trait data is available.
# From our previous steps, we know 'trait_row' was None, so trait data is not available.
# Hence, we skip linking, missing-value handling, and bias checks,
# but we still need to do final validation to mark it unusable.

if not is_trait_available:
    import pandas as pd

    print("Trait data is not available. Skipping link, missing-value handling, and bias checks.")
    # Provide a boolean for is_biased to avoid the ValueError in final validation.
    # The dataset is not usable because the trait is missing, so we can set is_biased=True.
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,     # gene data is available,
        is_trait_available=False,   # but trait data is missing
        is_biased=True,            # no valid trait -> not usable
        df=pd.DataFrame(),          # an empty DataFrame suffices here
        note="Trait data not found; dataset is not usable."
    )
    print("Dataset was not deemed usable due to missing trait data; final linked data not saved.")

else:
    # 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. Link the clinical and genetic data on sample IDs (requires clinical data from step 2)
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # 3. Handle missing values in linked data
    linked_data = handle_missing_values(linked_data, trait_col=trait)

    # 4. Determine bias
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)

    # 5. 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=True,
        is_biased=trait_biased,
        df=linked_data,
        note="Trait data and gene data successfully linked."
    )

    # 6. Save final linked data if usable
    if is_usable:
        linked_data.to_csv(out_data_file)
        print(f"Saved final linked data to {out_data_file}")
    else:
        print("Dataset was not deemed usable; final linked data not saved.")