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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Liver_Cancer"
cohort = "GSE212047"
# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE212047"
# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE212047.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE212047.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE212047.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"
# Get file paths for soft and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each clinical feature row
clinical_features = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# This is a microarray dataset of HSC cells, which should contain gene expression data
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# This is an animal study (mice) of specific genotypes, not a human study comparing disease cases vs. controls
# So trait data is not available in a way useful for our analysis
trait_row = None
age_row = None
gender_row = None
def convert_trait(x):
return None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save Metadata
# The dataset only has genetic data but no trait data for our analysis
is_trait_available = False if trait_row is None else True
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_row is None
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)
# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)
# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])
# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Looking at the row indices (gene identifiers), they appear to be numerical identifiers (10338001, etc.)
# rather than standard human gene symbols (which are usually alphanumeric like BRCA1, TP53, etc.)
# These appear to be probe IDs that will need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)
# Extract gene symbols from gene_assignment column
def extract_gene_symbol(assignment):
if pd.isna(assignment) or assignment == '---':
return None
# Get the second part after '//' which typically contains the gene symbol
parts = assignment.split('//')
if len(parts) >= 2:
return parts[1].strip()
return None
# Create mapping dataframe with ID and extracted gene symbols
mapping_df = pd.DataFrame({
'ID': gene_annotation['ID'],
'Gene': gene_annotation['gene_assignment'].apply(extract_gene_symbol)
})
# Preview the mapping data structure
print("Gene Mapping Preview:")
preview = preview_df(mapping_df)
print(json.dumps(preview, indent=2))
# Map gene identifiers between genetic data and annotation data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Print info about gene expression data after mapping
print("Gene Expression Data after Mapping:")
print(f"Number of genes: {len(gene_data)}")
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Create an empty dataframe since this is just a mouse study without usable trait data
empty_df = pd.DataFrame(columns=['trait'])
# Check bias and save metadata
is_biased = True # Set as biased since this is not even human data
note = "This is a mouse study without usable trait data for human disease analysis."
validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=is_biased,
df=empty_df,
note=note
) |