output/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/z1/preprocess/Angelman_Syndrome/GSE43900.csv"
out_gene_data_file = "./output/z1/preprocess/Angelman_Syndrome/gene_data/GSE43900.csv"
out_clinical_data_file = "./output/z1/preprocess/Angelman_Syndrome/clinical_data/GSE43900.csv"
json_path = "./output/z1/preprocess/Angelman_Syndrome/cohort_info.json"


# Step 1: Initial Data Loading
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)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# Determine data availability based on provided background and sample characteristics
is_gene_available = True  # Likely gene expression data (not miRNA/methylation)
trait_row = None          # No human trait (Angelman Syndrome) info available
age_row = None            # No age information
gender_row = None         # No gender information

# Conversion functions (defined for interface compatibility; not used since rows are None)
def _extract_value(x):
    if x is None:
        return None
    try:
        # Typical format: "key: value"
        parts = str(x).split(":", 1)
        return parts[1].strip() if len(parts) > 1 else str(x).strip()
    except Exception:
        return None

def convert_trait(x):
    # No trait information available in this dataset; return None
    return None

def convert_age(x):
    # No age information; return None
    return None

def convert_gender(x):
    # No gender information; return None
    return None

# Initial filtering metadata save
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
)

# Clinical feature extraction is skipped because trait_row is None
# (If trait_row were available, we would call geo_select_clinical_features and save the output.)