p3/preprocess/X-Linked_Lymphoproliferative_Syndrome/code/GSE190042.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE190042"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE190042"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE190042.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE190042.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE190042.csv"
json_path = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on the Series_summary, this is a transcriptome profiling dataset using Affymetrix PrimeView array
# This indicates gene expression data is available
is_gene_available = True 

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identification
trait_row = None  # No data about X-Linked Lymphoproliferative Syndrome 
age_row = 2  # Age data in row 2
gender_row = 1  # Gender data in row 1

# 2.2 Data Type Conversion Functions
def convert_age(x):
    if not x or ':' not in x:
        return None
    age_str = x.split(':')[1].strip()
    try:
        return float(age_str)
    except:
        return None

def convert_gender(x):
    if not x or ':' not in x:
        return None
    gender = x.split(':')[1].strip().upper()
    if gender == 'F':
        return 0
    elif gender == 'M':
        return 1
    return None

def convert_trait(x):
    return None  # Not used since trait data is not available

# 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=(trait_row is not None)
)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if i < 10:  # Print first 10 lines
            print(line.strip())
        elif "!series_matrix_table_begin" in line:
            print("\nFound table marker at line", i)
            # Print next 3 lines after marker
            for _ in range(3):
                print(next(f).strip())
            break
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure 
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Get gene mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Save the processed gene data
gene_data.to_csv(out_gene_data_file)
# Since trait_row is None, skip clinical feature extraction and data linking
# Just validate and save metadata about dataset being unusable due to lack of trait data
note = "Dataset contains gene expression data from multiple myeloma patients, but lacks data for X-linked lymphoproliferative syndrome."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True, 
    is_trait_available=False,
    is_biased=None,
    df=None,
    note=note
)

# No need to save any processed data since dataset isn't usable
# 1. Normalize gene symbols in gene expression data
genetic_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)

# 2. Validate and save metadata about dataset being unusable due to lack of trait data
note = "Dataset contains gene expression data from multiple myeloma patients, but lacks data for X-linked lymphoproliferative syndrome."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=False,
    df=genetic_data,
    note=note
)