p1/preprocess/Arrhythmia/code/GSE235307.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE235307"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE235307"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE235307.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE235307.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE235307.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. Gene Expression Data Availability
# Based on the series summary stating “Gene expression ...”, we set is_gene_available=True.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Assign row keys if data is available and non-constant.
# Observing the sample characteristics, we identify:
#  - trait_row: 5 (where we see "Atrial fibrillation" vs "Sinus rhythm")
#  - age_row: 2 (ages vary)
#  - gender_row: 1 (male/female are present)

trait_row = 5
age_row = 2
gender_row = 1

# 2.2 Define the conversion functions

def convert_trait(value: str) -> Optional[int]:
    """Convert 'cardiac rhythm after 1 year follow-up' to binary (0 or 1)."""
    # Extract the substring after colon
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()  # e.g. 'sinus rhythm', 'atrial fibrillation'
    if val == 'sinus rhythm':
        return 0
    elif val == 'atrial fibrillation':
        return 1
    else:
        return None

def convert_age(value: str) -> Optional[float]:
    """Convert the age string to float."""
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary (0 for Female, 1 for Male)."""
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'male':
        return 1
    elif val == 'female':
        return 0
    else:
        return None

# 3. Save Metadata using 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 (only if trait_row is not None)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview the selected clinical features
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)
    # Save the clinical features to CSV
    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])
# Observing the given identifiers (e.g., '4', '5', '6', etc.), they do not match typical human gene symbols.
# Therefore, they likely need to be mapped to recognized 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 columns in the gene_annotation dataframe corresponding to the probe IDs (matching gene_data.index)
#    and the gene symbols.
probe_id_column = "ID"
gene_symbol_column = "GENE_SYMBOL"

# 2. Get a gene mapping dataframe from the gene annotation
mapping_df = get_gene_mapping(
    gene_annotation,
    prob_col=probe_id_column,
    gene_col=gene_symbol_column
)

# 3. Convert probe-level measurements to gene-level expression data using the mapping
gene_data = apply_gene_mapping(gene_data, mapping_df)
import pandas as pd

# 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. Read the clinical DataFrame in a way that preserves the three rows (Arrhythmia, Age, Gender)
#    and interprets the first CSV row as the sample ID columns.
clinical_df = pd.read_csv(out_clinical_data_file, header=0)
# We know there are exactly 3 rows of data: [0]: Arrhythmia, [1]: Age, [2]: Gender
clinical_df.index = [trait, "Age", "Gender"]

# 3. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# 4. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 5. Check for bias in the trait and remove any biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Perform final 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="Trait data is available; completed linking and preprocessing."
)

# 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 linked data to {out_data_file}")
else:
    print("The dataset is not usable; skipping final data output.")