Abstract
Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale.
Highlights
Pathway Analysis (PA), known as functional enrichment analysis, is fast becoming one of the foremost tools of Omics research
PA methods seek to overcome the problem of interpreting overwhelmingly large lists of important, but isolated genes detached of biological context, which are the main output of most basic high-throughput data analysis, as differential expression analysis
Examples of PA methods that use an univariate approach can be found in Zeeberg et al (2003), Boorsma et al (2005), Subramanian et al (2005), and methods that use an multivariate approach can be found in Goeman et al (2004), Kong et al (2006), Hummel et al (2008), Jacob et al (2012)
Summary
Pathway Analysis (PA), known as functional enrichment analysis, is fast becoming one of the foremost tools of Omics research. PA methods have helped researchers in the identification of the biological roles of candidate genes, selected to design new therapies for cancer, circumventing collateral damage to healthy cells (Folger et al, 2011) Another instance is the determination of similarity and dissimilarity, at a molecular level, between sample groups, as in the comparison between cell lines and tumor samples (Heiser et al, 2012). Such kind of analyses may help researchers understand heterogeneity phenomena in different research contexts Another example is the use of PA methods to examine the biological function of gene modules, not yet validated sets of genes thought to be related between them, as in the analysis of genes that fluctuate in response to natural variations, like seasons (Dopico et al, 2015). All these applications have succeeded in specific goals, the use of PA methods may be as wide and complex as the creativity of their users
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