A Novel Method Incorporating Gene Ontology Information for Unsupervised Clustering and Feature Selection

Abstract

BackgroundAmong the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest.ResultsHere, we present a method that integrates gene ontology (GO) information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied to identify the genes that play a role in the cytotoxic responses of human hepatoblastoma cell line (HepG2) to saturated fatty acid (SFA) and tumor necrosis factor (TNF)-α, as compared to the non-toxic response to the unsaturated FFAs (UFA) and TNF-α. Incorporation of prior knowledge led to a better discrimination of the toxic phenotypes from the others. The model identified roles of lysosomal ATPases and adenylate cyclase (AC9) in the toxicity of palmitate. To validate the role of AC in palmitate-treated cells, we measured the intracellular levels of cyclic AMP (cAMP). The cAMP levels were found to be significantly reduced by palmitate treatment and not by the other FFAs, in accordance with the model selection of AC9.ConclusionsA framework is presented that incorporates prior ontology information, which helped to (a) perform unsupervised clustering of the phenotypes, and (b) identify the genes relevant to each cluster of phenotypes. We demonstrate the proposed framework by applying it to identify physiologically-relevant feature genes that conferred differential toxicity to saturated vs. unsaturated FFAs. The framework can be applied to other problems to efficiently integrate ontology information and expression data in order to identify feature genes.

Joint network and node selection for pathway-based genomic data analysis

“…Inspired by the seminal works in (Chuang et al , 2007; Fröhlich et al , 2006; Srivastava et al , 2008; Zycinski et al , 2013), we can use NaNOS in a variety of biomedical applications where there are abundant high-dimensional biomarkers of individual samples and other information sources—for example, the gene ontology (GO) and protein–protein interaction networks information—that capture correlation in the high-dimensional space. Here we discuss two approaches to apply NaNOS when we have only GO or other group information without network topology.…”

“…The first approach is to compute some distance or similarity scores between genes based on the GO information [e.g. following the approach by Srivastava et al (2008)] and then estimate the network topology based on a network learning method, for example, graphical lasso (Friedman et al , 2008). With the estimated network topology, we can compute the graph Laplacian matrices and apply NaNOS to select genes and groups of genes.…”

Synergistic effect of cAMP and palmitate in promoting altered mitochondrial function and cell death in HepG2 cells

“…In addition, cAMP was also reported to protect hepatocytes from bile acid [12, 13], Fas ligand [13, 14] and TNF-α [13, 15] induced apoptosis. Previous study in our group indicated that intracellular cAMP level in HepG2 cells was reduced significantly by palmitate, but not oleate or linoleate [16]. Therefore, we initially hypothesize that the down-regulation of cAMP by palmitate may play a role in the induction of cell death.…”

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

“…Using the available biological information on inter-connectivities and interactions between genes, we aim to discover pathways that are associated with a specific biological process. Srivastava et al [80] have employed the GO information into their priors, and Stingo et al [38] have used KEGG network information.…”