p3/preprocess/Height/code/GSE131835.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Height"
cohort = "GSE131835"

# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE131835"

# Output paths
out_data_file = "./output/preprocess/3/Height/GSE131835.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE131835.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE131835.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 the series title and summary, this is a gene expression profiling study using Affymetrix microarray
is_gene_available = True

# 2.1 Data Availability
# Height data is available in row 5
trait_row = 5
# Age data is available in row 3
age_row = 3  
# Gender data is available in row 2
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    try:
        # Extract value after 'height(cm): '
        return float(x.split(': ')[1])
    except:
        return None

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

def convert_gender(x):
    try:
        # Extract value after 'Sex: '
        gender = x.split(': ')[1].lower()
        return 1 if gender == 'male' else 0 if gender == 'female' else None
    except:
        return None

# 3. Save Metadata
is_usable = 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:
    selected_clinical_df = 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 data
    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)
# 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())
# The gene identifiers are in ENSEMBL format (ENSG...) followed by "_at"
# These need to be mapped to standard human 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))
# 1. Looking at gene_metadata preview, 'ID' matches gene expression index format (ENSG..._at)
# and 'ORF' contains gene symbols

# 2. Get probe-to-gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF')

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

# Preview the result
print("\nConverted gene expression data:")
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(selected_clinical_df, 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 adipose tissue 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)