p1/preprocess/Arrhythmia/code/GSE41177.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE41177"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE41177"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE41177.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE41177.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE41177.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 background info ("microarray analysis..."), we consider that
# this dataset likely contains gene expression data.
is_gene_available = True

# 2.1 Data Availability
# The trait (arrhythmia) appears to be constant in all samples (all have AF),
# hence it's not useful for association studies.
trait_row = None
# The 'age' variable is found at key=2 with multiple distinct values.
age_row = 2
# The 'gender' variable is found at key=1 with multiple distinct values.
gender_row = 1

# 2.2 Data Type Conversion
def convert_trait(value: str):
    # The trait is not actually available (constant across all samples),
    # so we return None here.
    return None

def convert_age(value: str):
    # Example entry: "age: 62Y"
    # We parse the substring after ':' then remove 'Y' and convert to float.
    try:
        parts = value.split(':', 1)
        age_str = parts[1].replace('Y', '').strip() if len(parts) > 1 else ''
        return float(age_str)
    except:
        return None

def convert_gender(value: str):
    # Example entry: "gender: male"
    # Convert female -> 0, male -> 1
    parts = value.split(':', 1)
    g_str = parts[1].strip().lower() if len(parts) > 1 else ''
    if g_str == 'male':
        return 1
    elif g_str == 'female':
        return 0
    return None

# 3. Save Metadata (Initial Filtering)
# trait_row is None => trait data is not available
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
# Since trait_row is None, we skip the clinical feature extraction step.
# 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 given probe IDs (e.g., "1007_s_at", "1053_at"), they are Affymetrix probe set IDs and not human gene symbols.
# Therefore, gene mapping to human gene symbols is required.

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 the columns corresponding to the probe identifiers and the gene symbols
#    From the annotation preview, the 'ID' column matches the probe identifiers in our gene_data,
#    and the 'Gene Symbol' column stores the gene symbols.

probe_col = "ID"
symbol_col = "Gene Symbol"

# 2. Extract the gene mapping DataFrame using the library function
gene_mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)

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

import pandas as pd

# 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}")

# Since trait_row is None (trait is not available), we cannot link clinical data or perform trait-based analysis.
# We'll skip linking and bias evaluation of the trait.

# We'll still perform the final validation to record that this cohort lacks trait data.
placeholder_df = pd.DataFrame()  # Empty placeholder
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # trait not available
    is_biased=False,           # No trait → can't evaluate trait bias, set to False to proceed
    df=placeholder_df,         # Required argument, though empty
    note="No trait data available in the cohort."
)

# If the dataset is usable (unlikely since trait is missing), we would save final linked data. 
if is_usable:
    # Normally we would have a "linked_data" DataFrame to save; 
    # however, there's no trait, so no final data is produced.
    pass
else:
    print("Trait not available; skipping final data linkage and output.")