p3/preprocess/LDL_Cholesterol_Levels/code/GSE181339.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "LDL_Cholesterol_Levels"
cohort = "GSE181339"

# Input paths
in_trait_dir = "../DATA/GEO/LDL_Cholesterol_Levels"
in_cohort_dir = "../DATA/GEO/LDL_Cholesterol_Levels/GSE181339"

# Output paths
out_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/GSE181339.csv"
out_gene_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/gene_data/GSE181339.csv"
out_clinical_data_file = "./output/preprocess/3/LDL_Cholesterol_Levels/clinical_data/GSE181339.csv"
json_path = "./output/preprocess/3/LDL_Cholesterol_Levels/cohort_info.json"

# Get paths for relevant files
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_path)

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

# Print dataset background information
print("Background Information:")
print(background_info)
print("\nClinical Features Overview:")
print(json.dumps(sample_chars, indent=2))
# 1. Gene Expression Data Availability
# Yes - the background information mentions RNA extraction, microarray experiments
is_gene_available = True

# 2.1 Data Availability
# LDL levels can be inferred from group (MONW has high LDL)
trait_row = 1
# Age data appears to be sample IDs rather than actual ages
age_row = None  
# Gender data is available in row 0
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    # Extract value after colon
    x = x.split(': ')[-1].strip()
    # MONW group has high LDL, other groups have normal LDL
    if x == 'MONW':
        return 1
    elif x in ['NW', 'OW/OB']:
        return 0
    return None

def convert_age(x):
    # Not used since age data unreliable
    return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    x = x.split(': ')[-1].strip()
    if x.lower() == 'woman':
        return 0
    elif x.lower() == 'man':
        return 1
    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:
    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 data
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save clinical data
    selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data
genetic_data = get_genetic_data(matrix_path)

# Preview raw data structure
print("First few rows of the raw data:")
print(genetic_data.head())

print("\nShape of the data:")
print(genetic_data.shape)

# Print first 20 row IDs to verify data structure 
print("\nFirst 20 probe/gene identifiers:")
print(list(genetic_data.index)[:20])
# From the pattern of gene identifiers being simple numbers like '7', '8', '15', etc.
# These appear to be probe IDs rather than human gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_path)

# Preview annotation data structure
print("Gene annotation data preview:")
print(preview_df(gene_metadata))
# Get mapping between gene IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Preview gene data 
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
print("\nShape of gene data:")
print(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. Check for biased features and remove biased demographic ones
# The function will print detailed distribution information
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save metadata about dataset quality
# The validation is affected by if the trait is biased, if the data has been filtered out, etc.
note = "This dataset compares gene expression between matched tumor and non-tumor kidney tissue samples."
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=trait_biased, df=linked_data, note=note)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)