p3/preprocess/Angelman_Syndrome/code/GSE43900.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Angelman_Syndrome"
cohort = "GSE43900"

# Input paths
in_trait_dir = "../DATA/GEO/Angelman_Syndrome"
in_cohort_dir = "../DATA/GEO/Angelman_Syndrome/GSE43900"

# Output paths
out_data_file = "./output/preprocess/3/Angelman_Syndrome/GSE43900.csv"
out_gene_data_file = "./output/preprocess/3/Angelman_Syndrome/gene_data/GSE43900.csv"
out_clinical_data_file = "./output/preprocess/3/Angelman_Syndrome/clinical_data/GSE43900.csv"
json_path = "./output/preprocess/3/Angelman_Syndrome/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on the series title and experimental design, this study examines gene expression
# in response to topoisomerase inhibitors in neurons. Therefore gene data is available.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 No trait, age or gender data available in sample characteristics
# All samples are cortical neurons from C57BL6 mice strain, not human data
trait_row = None
age_row = None 
gender_row = None

# 2.2 Define conversion functions (though not used in this case)
def convert_trait(x):
    return None

def convert_age(x):
    return None
    
def convert_gender(x):
    return None

# 3. Save Metadata
validate_and_save_cohort_info(is_final=False,
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=False)

# 4. Clinical Feature Extraction
# Skip since trait_row is None (no clinical data available)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# The identifiers appear to be probe IDs (starting with '172') rather than gene symbols
# These would need to be mapped to actual gene symbols for biological interpretation
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers")
print("'Gene Symbol' column: Contains gene symbols")
print("\nExample gene symbol value:")
print(gene_annotation['Gene Symbol'].iloc[0])
# Since this dataset contains mouse gene expression data (not human),
# we cannot use it for our human trait association study.
# Therefore we do not need to perform gene mapping.
# End preprocessing and mark as unusable in metadata.

validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False,
    is_biased=None,
    df=None,
    note="Dataset contains mouse gene expression data, not suitable for human trait studies."
)

print("Processing stopped: Dataset contains mouse gene expression data, not suitable for human trait studies.")