p3/preprocess/Height/code/GSE71994.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Height"
cohort = "GSE71994"

# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE71994"

# Output paths
out_data_file = "./output/preprocess/3/Height/GSE71994.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE71994.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE71994.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
is_gene_available = True  # Study mentions "genome-wide gene expression analysis" in PBMC

# 2.1 Data Availability
trait_row = 4  # Height data is in row 4
age_row = 3  # Age data is in row 3  
gender_row = 1  # Gender data is in row 1

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    try:
        # Extract numeric value after colon
        value = float(x.split(':')[1].strip())
        return value  # Height as continuous value in meters
    except:
        return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        # Extract numeric value after colon 
        value = int(x.split(':')[1].strip())
        return value  # Age as continuous value in years
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(':')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Save Initial Filtering Results
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. Extract Clinical Features
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 results
print(preview_df(clinical_df))

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# 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())
# Looking at the index/ID values (e.g. 7896746), these appear to be probe/array IDs rather than human gene symbols
# These numeric IDs 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_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. Identify columns for mapping 
# ID column matches probe IDs in gene expression data
# gene_assignment column contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')

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

# 3. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Print information about the resulting gene expression data
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few gene symbols:", list(gene_data.index[:10]))
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(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 PBMCs and height measurements from 40 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)