p1/preprocess/Atrial_Fibrillation/code/GSE115574.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE115574"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574"

# Output paths
out_data_file = "./output/preprocess/1/Atrial_Fibrillation/GSE115574.csv"
out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE115574.csv"
json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"

# STEP1
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("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True  # The series title and summary indicate "gene expression microarrays" were used

# 2. Variable Availability and Data Type Conversion

# 2.1 Identify rows for trait, age, gender.
# Examining the provided dictionary:
# {0: ['disease state: atrial fibrillation patient with severe mitral regurgitation',
#      'disease state: sinus rhythm patient with severe mitral regurgitation'],
#  1: ['tissue: left atrium  - heart', 'tissue: right atrium  - heart']}
# Row 0 has multiple unique values related to AF vs. sinus rhythm.
trait_row = 0
age_row = None    # No row provides age info
gender_row = None # No row provides gender info

# 2.2 Define conversion functions

def convert_trait(value: str) -> int:
    """Convert trait info to a binary indicator: AF=1, otherwise=0."""
    # Extract the part after the first colon
    parts = value.split(':', 1)
    raw_str = parts[1].strip().lower() if len(parts) > 1 else value.lower()
    if 'atrial fibrillation' in raw_str:
        return 1
    elif 'sinus rhythm' in raw_str:
        return 0
    else:
        return None

def convert_age(value: str) -> float:
    """Unused here because age_row is None, but define a placeholder."""
    return None

def convert_gender(value: str) -> int:
    """Unused here because gender_row is None, but define a placeholder."""
    return None

# 3. Save Metadata (initial filtering)
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. Clinical Feature Extraction
if trait_row is not None:
    selected_features = geo_select_clinical_features(
        clinical_df=clinical_data,  # Assumes 'clinical_data' DataFrame is already in environment
        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 features
    preview_dict = preview_df(selected_features, n=5, max_items=200)
    print("Clinical Features Preview:", preview_dict)

    # Save the extracted clinical features
    selected_features.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 probe IDs (e.g. '1007_s_at', '1053_at'), these are Affymetrix probe identifiers
# which require mapping to official 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 & 2. Identify the columns for the probe IDs and gene symbols in the annotation dataframe.
#    In this dataset, they are stored in 'ID' and 'Gene Symbol'.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Print the dimensions and the first 10 gene symbols to confirm
print("Mapped gene_data shape:", gene_data.shape)
print("First 10 gene symbols:", gene_data.index[:10])
# STEP7

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_features, normalized_gene_data)

# 3. Handle missing values systematically
linked_data_processed = handle_missing_values(linked_data, trait_col=trait)

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Final quality validation and metadata saving
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_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

# 6. If dataset is usable, save the final linked data
if is_usable:
    linked_data_final.to_csv(out_data_file)