p3/preprocess/Heart_rate/code/GSE12385.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Heart_rate"
cohort = "GSE12385"

# Input paths
in_trait_dir = "../DATA/GEO/Heart_rate"
in_cohort_dir = "../DATA/GEO/Heart_rate/GSE12385"

# Output paths
out_data_file = "./output/preprocess/3/Heart_rate/GSE12385.csv"
out_gene_data_file = "./output/preprocess/3/Heart_rate/gene_data/GSE12385.csv"
out_clinical_data_file = "./output/preprocess/3/Heart_rate/clinical_data/GSE12385.csv"
json_path = "./output/preprocess/3/Heart_rate/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 - Yes, this is whole genome microarray data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# Heart rate data is not available in this dataset
trait_row = None 

# Age data is available in row 1
age_row = 1

# Gender is available in row 0
gender_row = 0

# Conversion functions
def convert_trait(x):
    # Not used since trait data is not available
    return None

def convert_age(x):
    try:
        # Extract number after colon and convert to float
        age = float(x.split(": ")[1])
        return age
    except:
        return None

def convert_gender(x):
    # All samples are male so return 1
    return 1

# 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
# Skip this step since trait data is not available (trait_row is None)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 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())
# In this dataset, the identifiers appear to be numeric values (e.g. 1, 2, 3)
# These are probe IDs from a microarray platform which need to be mapped
# to human gene symbols for proper analysis.
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))
# In gene expression data, IDs are stored in the index
# In gene annotation data, looking at the columns, 'ID' matches these identifiers
# 'GENE_SYMBOL' contains the gene symbols we want to map to
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Save gene expression data to file
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)

# Print information about the mapping results
print("Original probe data shape:", genetic_data.shape)
print("Mapped gene data shape:", gene_data.shape)
print("\nFirst few gene symbols and their expression values:")
print(gene_data.head())
# 1. Normalize gene symbols
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)

# 2. Link clinical and genetic data together
# Since trait data is not available, we have no clinical data to link with
clinical_data = pd.DataFrame()
if not clinical_data.empty:
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
else:
    linked_data = pd.DataFrame()

# 3. Handle missing values if we have linked data 
if not linked_data.empty and trait in linked_data.columns:
    linked_data = handle_missing_values(linked_data, trait)

# 4. Judge whether features are biased and remove biased demographic features
is_biased = True  # No trait data means it's biased by default
if not linked_data.empty and trait in linked_data.columns:
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "This dataset lacks heart rate measurements. The study focused on gene expression changes in PBMCs before and after physical activity, but did not include heart rate as a measured variable."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    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)