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.
Highlights
Current methods of feature selection can be classified into two major categories: data-based and prior information-based
Most of the feature selection approaches belong to a family of supervised discriminative analysis and require labeling information of the phenotypes to identify feature genes
We demonstrated the proposed method by applying it to identify the genes that are likely involved in the toxicity of FFAs, in particular saturated (SFA), palmitate, and tumor necrosis factor (TNF)-a
Summary
Current methods of feature selection can be classified into two major categories: data-based and prior information-based. Examples of filtering techniques include the Wilcoxon’s rank sum test [1], Fisher’s Discriminant Analysis (FDA) [2,3], discriminative partial least squares (PLS) [4] or genetic algorithm (GA)- [5] based classification and clustering [6,7]. These techniques suffer from certain drawbacks, e.g., many among them are based on methods that require the genes to be independent and uncorrelated, which microarray data is not [8]. 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
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