p1/preprocess/Atrial_Fibrillation/code/GSE47727.py

# Path Configuration
from tools.preprocess import *

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

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

# Output paths
out_data_file = "./output/preprocess/1/Atrial_Fibrillation/GSE47727.csv"
out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE47727.csv"
out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE47727.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 platform (HumanHT-12 V3.0) indicates a typical gene expression microarray

# 2. Variable Availability
trait_row = None  # No row found containing AF or any case/control labels
age_row = 0       # Key 0 has multiple unique values for age
gender_row = 1    # Key 1 has at least two distinct values (male/female)

# 2.2 Data Type Conversion
def convert_trait(value: str):
    """
    Convert the trait value to a binary indicator (0 or 1).
    This dataset has no trait info, so we'll implement a placeholder function.
    """
    # Normally, we'd parse after the colon and map "AF" -> 1, "control" -> 0, else None
    return None

def convert_age(value: str):
    """
    Convert the 'age (yrs)' string to a float.
    Returns None if the format is unexpected.
    """
    try:
        # Split at the colon, take the part after the colon, strip, and convert to float
        parts = value.split(':')
        if len(parts) < 2:
            return None
        return float(parts[1].strip())
    except:
        return None

def convert_gender(value: str):
    """
    Convert gender to binary: female -> 0, male -> 1.
    Returns None if the format is unexpected.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    gender_str = parts[1].strip().lower()
    if gender_str == 'female':
        return 0
    elif gender_str == 'male':
        return 1
    else:
        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
# Skip this step because trait_row is None (no trait data available)
# 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 IDs (e.g., "ILMN_1343291") appear to be Illumina probe IDs, not standard human gene symbols.
# Therefore, this data requires mapping to gene symbols.

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 in gene_annotation that match the probe IDs from gene_data (the 'ID' column),
#    and the column that holds gene symbols (the 'Symbol' column).
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# (Optional) Print some basic info for verification.
print("Mapped gene_data shape:", gene_data.shape)
print("First 20 gene symbols after mapping:")
print(gene_data.index[:20])