p3/preprocess/X-Linked_Lymphoproliferative_Syndrome/code/GSE180393.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE180393"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE180393"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE180393.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE180393.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE180393.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
# Yes - based on series info this is microarray gene expression data on Affymetrix ST2.1 platform
is_gene_available = True

# 2.1 Data Availability & 2.2 Data Type Conversion 

# For trait:
# Row 0 contains "sample group" which indicates disease status
trait_row = 0

def convert_trait(value: str) -> Optional[int]:
    if not isinstance(value, str):
        return None
    # Extract value after colon
    if ':' in value:
        value = value.split(':', 1)[1].strip()
    # Living donor = 0 (control), all disease conditions = 1 
    if 'Living donor' in value:
        return 0
    return 1  # All other values indicate disease conditions

# Age and gender data not available in sample characteristics
age_row = None
gender_row = None

def convert_age(value: str) -> Optional[float]:
    return None

def convert_gender(value: str) -> Optional[int]:
    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=bool(trait_row is not None))

# 4. Extract clinical features
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age, 
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    clinical_features.to_csv(out_clinical_data_file)
# 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
# Based on the gene identifiers shown (e.g. '100009613_at', '10000_at'), these appear to be Affymetrix probe IDs
# from the microarray platform mentioned in the metadata rather than standard human gene symbols.
# Therefore they will need to be mapped to gene symbols.

requires_gene_mapping = True
# First inspect raw SOFT file content
print("First 50 lines of SOFT file:")
with gzip.open(soft_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if i < 50:  # Print first 50 lines 
            print(line.strip())
        elif i == 50:
            print("...\n")

# Extract gene annotation 
gene_annotation = get_gene_annotation(soft_file_path)

# Print number of rows and columns
print(f"\nShape of annotation data: {gene_annotation.shape}")
print("\nColumn names in annotation data:")
print(gene_annotation.columns.tolist())

# Print first few entries
print("\nPreview of annotation data:")
print(gene_annotation.head())
# Get gene annotation using the provided function
gene_annotation = get_gene_annotation(soft_file_path)

# Create mapping between probe IDs and gene symbols through Entrez IDs
prob_to_entrez = gene_annotation[['ID', 'ENTREZ_GENE_ID']].dropna()
entrez_to_symbol = pd.read_csv('https://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz',
                            sep='\t', compression='gzip',
                            usecols=['GeneID', 'Symbol']).rename(columns={'GeneID': 'ENTREZ_GENE_ID', 'Symbol': 'Gene'})

# Get final mapping and proceed with gene data conversion
mapping_df = prob_to_entrez.merge(entrez_to_symbol, on='ENTREZ_GENE_ID', how='left')[['ID', 'Gene']].dropna()

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

# Normalize gene symbols to official ones
gene_data = normalize_gene_symbols_in_index(gene_data)

print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head())

# Save gene data
gene_data.to_csv(out_gene_data_file)
# Get gene annotation using the provided function
gene_annotation = get_gene_annotation(soft_file_path)

# Create mapping between probe IDs and gene symbols through Entrez IDs
prob_to_entrez = gene_annotation[['ID', 'ENTREZ_GENE_ID']].dropna()
prob_to_entrez['ENTREZ_GENE_ID'] = prob_to_entrez['ENTREZ_GENE_ID'].astype(str)

entrez_to_symbol = pd.read_csv('https://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz',
                            sep='\t', compression='gzip',
                            usecols=['GeneID', 'Symbol'])
entrez_to_symbol['GeneID'] = entrez_to_symbol['GeneID'].astype(str)

# Get final mapping and proceed with gene data conversion
mapping_df = prob_to_entrez.merge(entrez_to_symbol, 
                                 left_on='ENTREZ_GENE_ID', 
                                 right_on='GeneID', 
                                 how='left')[['ID', 'Symbol']].dropna()
mapping_df = mapping_df.rename(columns={'Symbol': 'Gene'})

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

# Normalize gene symbols to official ones
gene_data = normalize_gene_symbols_in_index(gene_data)

print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head())

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 1. Gene data was already normalized in previous step
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_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 kidney disease 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=gene_data,
    note=note
)
# 1. Gene expression data was normalized in step 7 and stored in genetic_data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)

# 2. Save metadata indicating trait data is unavailable 
note = "Dataset contains gene expression data from kidney disease 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
)