p1/preprocess/Arrhythmia/code/GSE47727.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE47727"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE47727"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE47727.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE47727.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE47727.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
is_gene_available = True  # The platform is Illumina HumanHT-12, indicating gene expression data.

# 2. Variable Availability and Data Type Conversion

# Based on the sample characteristics:
#   0 -> "age (yrs): X"
#   1 -> "gender: male"/"gender: female"
#   2 -> "tissue: blood"
#
# There's no indication (key) that provides Arrhythmia status; all subjects are "control participants."
# Thus, the trait is NOT available in this dataset.
trait_row = None
age_row = 0
gender_row = 1

# Data type conversion functions
def convert_trait(x: str):
    """
    For demonstration, we define the conversion but this dataset
    has no trait data. We return None to indicate unavailability.
    """
    return None

def convert_age(x: str):
    """
    Extract the numeric age after 'age (yrs):' and convert to float.
    If there is any parsing error, return None.
    """
    # Typically, these entries look like "age (yrs): 67"
    # We split by ':' and strip.
    try:
        val = x.split(':')[-1].strip()
        return float(val)
    except:
        return None

def convert_gender(x: str):
    """
    Convert 'gender: female' -> 0 and 'gender: male' -> 1.
    If other strings appear, return None.
    """
    try:
        val = x.split(':')[-1].strip().lower()
        if val == 'male':
            return 1
        elif val == 'female':
            return 0
    except:
        pass
    return None

# 3. Save Metadata
# Perform initial filtering (is_final=False). Since trait_row is None,
# trait data is NOT available.
is_trait_available = False

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 extracting clinical features.
# 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])
# The gene identifiers (e.g., "ILMN_1343291") are Illumina probe IDs, not standard human gene symbols.
# Thus, they require mapping to 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. We identify that in the gene_annotation DataFrame:
#    - The "ID" column matches the Illumina probe IDs (e.g., "ILMN_1343291") found in gene_data.index.
#    - The "Symbol" column holds gene symbol information for mapping.

# 2. Get a gene mapping DataFrame from gene_annotation by selecting the 'ID' column as the probe identifier
#    and the 'Symbol' column as the gene symbol.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe-level measurements in gene_data to gene-level measurements
#    by applying the gene mapping, handling probes with multiple gene mappings.
gene_data = apply_gene_mapping(gene_data, 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.")