Abstract
BackgroundEnrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation.ResultsWe present the EnrichmentBrowser package as an easily applicable software that enables (1) the application of the most frequently used set-based and network-based enrichment methods, (2) their straightforward combination, and (3) a detailed and interactive visualization and exploration of the results. The package is available from the Bioconductor repository and implements additional support for standardized expression data preprocessing, differential expression analysis, and definition of suitable input gene sets and networks.ConclusionThe EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. It combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0884-1) contains supplementary material, which is available to authorized users.
Highlights
Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations
The first generation of methods is centered around the traditionally used overrepresentation analysis, which tests based on the hypergeometric distribution whether genes above a predefined significance threshold are overrepresented in functional gene sets [3]
We introduce competitive ranks rC calculated as the percentage of gene sets with a value of the ranking statistic at least as extreme as observed for the gene set to be ranked
Summary
Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Genome-wide gene expression studies with microarrays or RNA-seq typically measure several thousand genes at a time [1]. Analysis focuses on whether disproportionately many of the remaining genes belong to known functional sets of genes. Such an enrichment for certain gene functions, sets or pathways immediately generates important hypotheses about. The first generation of methods is centered around the traditionally used overrepresentation analysis, which tests based on the hypergeometric distribution whether genes above a predefined significance threshold are overrepresented in functional gene sets [3]. The second generation of methods resolves the restriction to the subset of significant genes, and instead scores the tendency of gene set members to appear rather at the top or bottom of the ranked list of all measured genes [4]
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