p3/preprocess/Height/code/GSE117525.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Height"
cohort = "GSE117525"

# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE117525"

# Output paths
out_data_file = "./output/preprocess/3/Height/GSE117525.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE117525.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE117525.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
# Based on background info mentioning "skeletal muscle transcriptome" and "gene expression profiling"
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identifiers
trait_row = 4  # Height data in row 4
age_row = 3    # Age data in row 3  
gender_row = 1 # Gender data in row 1

# 2.2 Conversion functions
def convert_trait(x):
    if pd.isna(x):
        return None
    try:
        # Extract height value after colon and convert to float
        height = float(x.split(': ')[1])
        return height
    except:
        return None

def convert_age(x):
    if pd.isna(x):
        return None
    try:
        # Extract age value and convert to float
        age = float(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    try:
        # Extract gender value after colon
        gender = x.split(': ')[1].strip().upper()
        if gender == 'F':
            return 0
        elif gender == 'M':
            return 1
        return None
    except:
        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 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 to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
with gzip.open(matrix_file_path, 'rt') as file:
    for i, line in enumerate(file):
        if "!series_matrix_table_begin" in line:
            skip_rows = i + 1
            break

genetic_data = pd.read_csv(matrix_file_path, compression='gzip', skiprows=skip_rows-1, 
                          sep='\t', comment='!', header=0, index_col=0)

# Print information about the data structure
print("First few rows of the genetic data:")
print(genetic_data.head())
print("\nShape of genetic data:", genetic_data.shape)
print("\nColumn names:", genetic_data.columns.tolist())
# Looking at the identifiers like "100009676_at", "10000_at", "10001_at", etc.
# These appear to be probe IDs from a microarray platform rather than standard human gene symbols
# The "_at" suffix is characteristic of Affymetrix arrays
# These identifiers will need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Parse the Description column to extract gene symbols
mapping_data = pd.DataFrame({
    'ID': gene_metadata['ID'],
    'Gene': gene_metadata['Description'].apply(lambda x: x.split(',')[0].strip() if pd.notna(x) else None)
})

print("Preview of gene mapping data:")
print(json.dumps(preview_df(mapping_data), indent=2))
# Get gene mapping dataframe from gene annotation data
# ID column matches the probe IDs in gene expression data (e.g. "100009676_at")
# Description column contains gene names that we need to map to
mapping_df = pd.DataFrame({
    'ID': gene_metadata['ID'],
    'Gene': gene_metadata['Description'].apply(lambda x: extract_human_gene_symbols(x)[0] if pd.notna(x) and extract_human_gene_symbols(x) else None)
})

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

# Preview the mapped gene data
print("\nPreview of gene expression data after mapping:")
print(gene_data.head())
print("\nShape of gene expression data:", gene_data.shape)
# 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(clinical_features, gene_data)

# 3. Clean height measurements - filter out weight values that were incorrectly recorded as height
linked_data = linked_data[linked_data[trait] < 3.0]  # Keep only plausible height values in meters

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

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

# 6. Final validation and save metadata
note = "Dataset contains gene expression data from muscle tissue. Original height measurements had inconsistent units - filtered to keep only plausible meter values."
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
)

# 7. 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)