p3/preprocess/Glioblastoma/code/GSE249289.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glioblastoma"
cohort = "GSE249289"

# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE249289"

# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE249289.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE249289.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE249289.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 check
is_gene_available = True  # Based on series title and design, this is gene expression data

# 2. Data availability and conversion functions
trait_row = None  # No direct disease status as all samples are glioblastoma
age_row = 2  # Age information is in row 2
gender_row = 1  # Gender information is in row 1

def convert_age(x):
    try:
        # Extract numeric value after colon
        age = int(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    try:
        # Extract value after colon and convert to binary
        gender = x.split(': ')[1].lower()
        if gender == 'female':
            return 0
        elif gender == 'male':
            return 1
        return None
    except:
        return None

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

# 3. Save metadata
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)

# 4. Extract clinical features if available
# Skip this step since trait_row is None
# 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)
# The identifiers start with "ILMN_" which indicates they are Illumina probe IDs
# These need to be mapped to official human gene symbols for analysis
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))
# Get mapping dataframe with ID and Symbol columns from gene annotation
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Print shape and preview to verify the mapping result
print("Shape of gene expression data after mapping:", 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. Extract clinical features and link with genetic data
clinical_features = geo_select_clinical_features(
    clinical_data, 
    trait=trait,
    trait_row=None, 
    age_row=2,
    convert_age=convert_age,
    gender_row=1, 
    convert_gender=convert_gender
)

linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, "Age")  # Use Age as primary feature since trait is not available

# 4. Check for biased features and remove them if needed
is_biased = False  # Only demographic features, no trait to be biased
linked_data = judge_and_remove_biased_features(linked_data, "Age")[1]  # Use Age since trait is not available

# 5. Validate and save cohort info
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=linked_data,
    note="Contains gene expression data with age and gender information, but no trait data for analysis"
)

# 6. Save linked data with demographic features
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file)
# 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. Record metadata about dataset's unavailability for trait analysis
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="Contains gene expression data from glioblastoma tumorspheres but no control samples for trait analysis"
)