p3/preprocess/Glioblastoma/code/GSE39144.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glioblastoma"
cohort = "GSE39144"

# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE39144"

# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE39144.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE39144.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE39144.csv"
json_path = "./output/preprocess/3/Glioblastoma/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
# GeneChip Human Genome U133 Plus 2.0 Array was used, indicating gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability

# For trait: Feature 0 contains "cell type: glioma-initiating cells" which indicates glioblastoma samples
trait_row = 0  

# Age: No age information available in sample characteristics 
age_row = None

# Gender: Feature 2 contains gender information
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    # Convert glioma vs non-glioma
    if "glioma-initiating cells" in value.lower():
        return 1
    return 0

def convert_age(value: str) -> float:
    # Not used but defined for completeness
    return None

def convert_gender(value: str) -> int:
    # Extract value after colon and convert to binary
    if ":" in value:
        gender = value.split(":")[1].strip().lower()
        if gender == "female":
            return 0
        elif gender == "male":
            return 1
        elif "pooled female" in gender:
            return 0
        elif "pooled male" in gender:
            return 1
    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=(trait_row is not None)
)

# 4. Clinical Feature Extraction
# Extract clinical features since trait_row is not None
clinical_df = 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 extracted features
preview_result = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview_result)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# 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)
# Examine the gene identifiers - these are Affymetrix probe IDs that require mapping to gene symbols
# Format: XXXXX_at, XXXXX_s_at, XXXXX_x_at etc.
# These are probe set IDs from Affymetrix arrays, not gene symbols
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. Identify relevant columns from gene annotation
# 'ID' column matches probe IDs in gene expression data
# 'Gene Symbol' column contains the gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Preview result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
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=is_biased,
    df=linked_data,
    note="Gene expression data from hiPSCs, hESCs and differentiated cells, including glioblastoma cells"
)

# 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)