p3/preprocess/Height/code/GSE106800.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Height"
cohort = "GSE106800"

# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE106800"

# Output paths
out_data_file = "./output/preprocess/3/Height/GSE106800.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE106800.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE106800.csv"
json_path = "./output/preprocess/3/Height/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)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# From background info: "Microarray analysis was performed on skeletal muscle biopsies"
# This indicates gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# Height data is available in row 3
trait_row = 3

def convert_trait(x):
    try:
        # Extract numeric value after colon and space
        return float(x.split(': ')[1])
    except:
        return None

# Age data is available in row 2
age_row = 2

def convert_age(x):
    try:
        return float(x.split(': ')[1])
    except:
        return None

# Gender data is available in row 0 but only one value (male)
gender_row = None  # Constant features are not useful

def convert_gender(x):
    try:
        val = x.split(': ')[1].lower()
        if val == 'male':
            return 1
        elif val == 'female':
            return 0
        return None
    except:
        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
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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# Review the gene identifiers
# The identifiers are numeric codes like '16650001', '16650003' etc.
# These are not standard gene symbols (like BRCA1, TNF etc.)
# They appear to be probe IDs that need to be mapped to gene symbols

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

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Get gene mapping dataframe from gene annotation
# 'ID' column in gene_metadata contains probe IDs that match gene expression data
# 'gene_assignment' contains gene symbols in a messy format
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

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

# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = geo_select_clinical_features(
    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
)

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

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from skeletal muscle biopsies and height measurements from subjects"
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=note
)

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