Detecting networks of genes associated with human drug induced liver injury (DILI) concern using sparse principal components

Abstract

The 12th Annual International Conference on the Critical Assessment of Massive Data Analysis (CAMDA) used data from the massive Japanese Toxicogenomics Project (TGP) to predict drug-induced liver injury (DILI) concern provided by the U.S. Food and Drug Administration (FDA). The challenge was to predict DILI concern by means of gene expression data. Analysis of this high-dimensional toxicogenomic data requires statistical methodologies that can detect the transcriptomic associations with toxicity. We propose an analysis technique that involves sparse principal component analysis to efficiently reduce the dimension of the analysis problem. Sparse principal component variables are composed of groups of expressed genes. Associations between DILI concern and sparse principal component variables were tested and further scrutinized with sparse regression methodology to identify concise transcriptomic structures potentially responsible for and predictive of drug toxicity. Working with a subset of the TGP data with FDA DILI concern classification, we identified 5 transcriptomic structures (sparse principal component variables) statistically associated with DILI concern. The most statistically significant structure consists of the genes ZBTB16, FLVCR2, TNS3, and ASB13. Sparse statistical methods offer a new way to handle analysis issues with massive omic data. Sparse PCA can efficiently extract groups of transcriptomic markers that may indicate drug toxicity.