p3/preprocess/Kidney_stones/code/GSE123993.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_stones"
cohort = "GSE123993"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_stones"
in_cohort_dir = "../DATA/GEO/Kidney_stones/GSE123993"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_stones/GSE123993.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_stones/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_stones/clinical_data/GSE123993.csv"
json_path = "./output/preprocess/3/Kidney_stones/cohort_info.json"

# Get paths for relevant files
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_path)

# Get unique values for each clinical feature
sample_chars = get_unique_values_by_row(clinical_data)

# Print dataset background information
print("Background Information:")
print(background_info)
print("\nClinical Features Overview:")
print(json.dumps(sample_chars, indent=2))
# 1. Gene Expression Data Availability
# Since the background information mentions "whole genome gene expression profiling using Affymetrix HuGene 2.1ST arrays",
# this indicates gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability 
# The trait can be determined from intervention group (key 3)
trait_row = 3
# Age is not available in the characteristics data, though subjects are known to be >65 years old from background
age_row = None  
# Gender is available in key 1
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Extract value after colon and trim whitespace
    if ":" not in value:
        return None
    value = value.split(":", 1)[1].strip()
    # Convert intervention group to binary: 1 for 25(OH)D3 treatment, 0 for placebo
    if "25-hydroxycholecalciferol" in value or "25(OH)D3" in value:
        return 1
    elif "Placebo" in value:
        return 0
    return None

def convert_gender(value):
    if ":" not in value:
        return None
    value = value.split(":", 1)[1].strip()
    # Convert gender to binary: 0 for female, 1 for male
    if value.lower() == "female":
        return 0
    elif value.lower() == "male":
        return 1
    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. Clinical Feature Extraction
if trait_row is not None:
    # Extract clinical features 
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data
genetic_data = get_genetic_data(matrix_path)

# Preview raw data structure
print("First few rows of the raw data:")
print(genetic_data.head())

print("\nShape of the data:")
print(genetic_data.shape)

# Print first 20 row IDs to verify data structure 
print("\nFirst 20 probe/gene identifiers:")
print(list(genetic_data.index)[:20])
# These IDs appear to be probe IDs rather than human gene symbols
# They follow a numeric pattern (16650XXX) which is characteristic of microarray probe IDs
# Gene symbols are typically alphabetic like BRCA1, TP53 etc.
# Therefore these identifiers need to be mapped to standard gene symbols

requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_path)

# Preview annotation data structure
print("Gene annotation data preview:")
print(preview_df(gene_metadata))
# 1. Identify relevant columns: ID for probe identifiers and gene_assignment for gene symbols
id_col = 'ID'
gene_col = 'gene_assignment'

# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, id_col, gene_col)

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

# Print info about the gene data
print("\nShape of gene expression data after mapping:")
print(gene_data.shape)
print("\nFirst few rows and columns of mapped gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
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 biased features and remove biased demographic ones
# The function will print detailed distribution information
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save metadata about dataset quality
# The validation is affected by if the trait is biased, if the data has been filtered out, etc.
note = "This dataset compares gene expression between matched tumor and non-tumor kidney tissue samples."
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:
    linked_data.to_csv(out_data_file)