p1/preprocess/Arrhythmia/code/GSE93101.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE93101"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE93101"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE93101.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE93101.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE93101.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. Decide whether gene expression data is available
# From the background information, this submission represents transcriptome data.
is_gene_available = True

# 2. Identify rows for trait, age, and gender, and define conversion functions.
#   - trait_row, age_row, gender_row
trait_row = 0  # "course:" with multiple diseases listed, including "Arrhythmia"
age_row = 1    # "age:"
gender_row = 2 # "gender:"

def convert_trait(value: str) -> Optional[int]:
    """Convert the 'course' field to a binary variable: 1 if Arrhythmia, 0 otherwise."""
    try:
        # Example: "course: Arrhythmia"
        val = value.split(":")[1].strip().lower()
        return 1 if val == "arrhythmia" else 0
    except IndexError:
        return None

def convert_age(value: str) -> Optional[float]:
    """Convert the 'age' field to a float."""
    try:
        # Example: "age: 55.8"
        val = value.split(":")[1].strip()
        return float(val)
    except (IndexError, ValueError):
        return None

def convert_gender(value: str) -> Optional[int]:
    """Convert the 'gender' field to 0 (Female) or 1 (Male)."""
    try:
        # Example: "gender: F"
        val = value.split(":")[1].strip().lower()
        if val == "f":
            return 0
        elif val == "m":
            return 1
        else:
            return None
    except IndexError:
        return None

# 3. Perform initial filtering and save metadata
#    Trait data availability is inferred from whether trait_row is None.
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. If the trait data is available, extract and preview clinical features
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,          # Assume 'clinical_data' is our previously obtained pandas DataFrame
        trait,                  # Global variable: "Arrhythmia"
        trait_row,
        convert_trait,
        age_row,
        convert_age,
        gender_row,
        convert_gender
    )

    # Preview the extracted clinical features
    preview = preview_df(selected_clinical_df, n=5)
    print(preview)

    # Save the clinical features to file
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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 the provided identifiers (e.g., "ILMN_1651209", "ILMN_1651228"), they appear to be Illumina probe IDs, not standard human gene symbols.
# Therefore, mapping to gene symbols is needed.

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 correspond to the probe ID and the gene symbol.
#    From the preview, the 'ID' column in 'gene_annotation' matches the expression data's row index,
#    and the 'Symbol' column appears to store the gene symbol.
probe_id_col = "ID"
gene_symbol_col = "Symbol"

# 2. Get the gene mapping dataframe from the annotation.
mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)

# 3. Convert probe-level measurements to gene-level expression data.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 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. Use the 'selected_clinical_df' variable from step 2 to link clinical and genetic data
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 (and potentially other features) 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,  # We do have a trait column
    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.")