p3/preprocess/Vitamin_D_Levels/code/GSE34450.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE34450"

# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE34450"

# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE34450.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE34450.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE34450.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/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. Check gene expression data availability
# Based on the title "Genes Associated with MUC5AC Expression" and studying effect on small airway epithelium gene expression,
# this is likely a gene expression dataset
is_gene_available = True

# 2. Analyze variable availability and create conversion functions
# 2.1 & 2.2 Vitamin D level data is in row 3 with "serum 25-oh-d" prefix
trait_row = 3
def convert_trait(x):
    if pd.isna(x):
        return None
    x = str(x).lower()
    if "serum 25-oh-d:" not in x:
        return None 
    if "high" in x:
        return 2
    elif "mid" in x:
        return 1
    elif "low" in x:
        return 0
    return None

# Age is not available
age_row = None
def convert_age(x):
    return None

# Gender is not available 
gender_row = None
def convert_gender(x):
    return None

# 3. Save cohort info and validate 
# trait_row is not None, so trait data is available
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, 
                            is_gene_available=is_gene_available, 
                            is_trait_available=True)

# 4. Extract clinical features and save since trait data is available
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 output
print("\nPreviewing clinical features:")
print(preview_df(clinical_features))

# Save to CSV
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 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These are Affymetrix probe IDs (e.g. '1007_s_at') from an Affymetrix microarray
# They need to be mapped to human gene symbols for analysis
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)
# From previews: gene expression data uses IDs like '1007_s_at'
# Gene annotation has matching IDs in 'ID' column and gene symbols in 'Gene Symbol' column

# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Print shape and preview first few rows
print("\nGene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

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

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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_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)