Knowledge Driven Variable Selection (KDVS) – a new approach to enrichment analysis of gene signatures obtained from high–throughput data

Abstract

BackgroundHigh–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power.ResultsWe present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method.ConclusionsWe showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment–based approaches.

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.…”

Enhancing Interpretability of Gene Signatures with Prior Biological Knowledge

“…By using the microarray annotation, KDVS builds the mapping from the probeset list to the GO terms and vice versa to allow fast querying in both directions. Then, for each GO term t , it generates a $p_s\times n$ submatrix of gene expression data, with $p_s\ll p$, where only the expression values related to genes annotated to t are retained [3]. By construction, the overlap of each pair of submatrices is the same of the corresponding GO terms.…”

“…Recently, Knowledge Driven Variable Selection (KDVS) [ 3 ], an alternative pipeline that uses GO a priori as the established domain knowledge, has been proposed.…”

Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

“…An additional similar study that applied KDVS to SVM-RNE is presented by Ref. [ 38 ], in which the authors proposed a framework that uses a priori biological knowledge in high-throughput data analysis.…”