p3/preprocess/X-Linked_Lymphoproliferative_Syndrome/code/GSE156309.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE156309"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE156309"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE156309.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE156309.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE156309.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
is_gene_available = True  # The background info shows this is Affymetrix gene expression microarray data

# 2.1 Data Availability
age_row = 0  # Age data is in row 0
trait_row = 3  # Disease status (relapse) is in row 3
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_age(x):
    try:
        # Extract number after "age: "
        age = int(x.split(": ")[1])
        return age
    except:
        return None

def convert_trait(x):
    # Convert relapse status to binary
    try:
        status = x.split(": ")[1].lower()
        if "relapse-free" in status:
            return 0
        elif "relapse" in status:
            return 1
        return None
    except:
        return None
    
def convert_gender(x):
    # Not used as gender data is not available
    return None

# 3. Save Metadata
is_usable = 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
)

# 4. Clinical Feature Extraction
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
    )
    
    # Preview the processed data
    print("Preview of processed clinical features:")
    print(preview_df(clinical_features))
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    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
# Looking at the IDs like '1007_s_at', '1053_at', these are Affymetrix probe IDs from the U133 Plus 2.0 array platform
# This is also confirmed in the metadata: "Affymetrix Human U133 Plus 2.0 microarrays"
# These probe IDs need to be mapped to official gene symbols
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)
# 1. From observation of IDs:
# In gene_annotation: 'ID' column has values like '1007_s_at'
# In genetic_data: index has same format like '1007_s_at'
# Gene symbols are in 'Gene Symbol' column

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Preview result
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
# 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. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for bias in features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save metadata about dataset
note = "Dataset contains gene expression data from DLBCL patients, measuring relapse status as trait."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)