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

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.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  J. Davis.  Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2007 .

[3]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Klaus Obermayer,et al.  A new summarization method for affymetrix probe level data , 2006, Bioinform..

[5]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[6]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[7]  Ashley J. Bonner.  Sparse Principal Component Analysis for High-Dimensional Data: A Comparative Study , 2012 .

[8]  R. Tibshirani,et al.  A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.

[9]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[10]  H. Yamada,et al.  The Japanese toxicogenomics project: application of toxicogenomics. , 2010, Molecular nutrition & food research.

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[13]  R. Tibshirani,et al.  Sparse Principal Component Analysis , 2006 .

[14]  Weida Tong,et al.  FDA-approved drug labeling for the study of drug-induced liver injury. , 2011, Drug discovery today.

[15]  P. Bushel,et al.  The evolution of bioinformatics in toxicology: advancing toxicogenomics. , 2011, Toxicological sciences : an official journal of the Society of Toxicology.

[16]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[17]  John P. Overington,et al.  Toxicogenomics Investigation Under the eTOX Project , 2012 .

[18]  Woojoo Lee,et al.  Super-sparse principal component analyses for high-throughput genomic data , 2010, BMC Bioinformatics.

[19]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Mark D. Johnson,et al.  Drug-induced oxidative stress in rat liver from a toxicogenomics perspective. , 2005, Toxicology and applied pharmacology.