1 Introduction to rrvgo

Gene Ontologies (GO) are often used to guide the interpretation of high-throughput omics experiments, with lists of differentially regulated genes being summarized into sets of genes with a common functional representation. Due to the hierachical nature of Gene Ontologies, the resulting lists of enriched sets are usually redundant and difficult to interpret.

rrvgo aims at simplifying the redundance of GO sets by grouping similar terms based on their semantic similarity. It also provides some plots to help with interpreting the summarized terms.

This software is heavily influenced by REVIGO. It mimics a good part of its core functionality, and even some of the outputs are similar. Without aims to compete, rrvgo tries to offer a programatic interface using available annotation databases and semantic similarity methods implemented in the Bioconductor project.

2 Using rrvgo

2.1 Getting started

Starting with a list of genes of interest (eg. coming from a differential expression analysis), apply any method for the identification of eneriched GO terms (see GOStats or GSEA).

rrvgo does not care about genes, but GO terms. The input is a vector of enriched GO terms, along with (recommended, but not mandatory) a vector of scores. If scores are not provided, rrvgo takes the GO term (set) size as a score, thus favoring broader terms.

2.2 Calculating the similarity matrix and reducing GO terms

First step is to get the similarity matrix between terms. The function calculateSimMatrix takes a list of GO terms for which the semantic simlarity is to be calculated, an OrgDb object for an organism, the ontology of interest and the method to calculate the similarity scores.

go_analysis <- read.delim(system.file("extdata/example.txt", package="rrvgo"))
simMatrix <- calculateSimMatrix(go_analysis$ID,

The semdata parameter (see ?calculateSimMatrix) is not mandatory as it is calculated on demand. If the function needs to run several times with the same organism, it’s advisable to save the GOSemSim::godata(orgdb, ont=ont) object, in order to reuse it between calls and speedup the calculation of the similarity matrix.

From the similarity matrix one can group terms based on similarity. rrvgo provides the reduceSimMatrix function for that. It takes as arguments i) the similarity matrix, ii) an optional named vector of scores associated to each GO term, iii) a similarity threshold used for grouping terms, and iv) an orgdb object.

scores <- setNames(-log10(go_analysis$qvalue), go_analysis$ID)
reducedTerms <- reduceSimMatrix(simMatrix,

reduceSimMatrix groups terms which are at least within a similarity below threshold, and selects as the group representative the term with the higher score within the group. In case the vector of scores is not available, reduceSimMatrix can either use the uniqueness of a term (default), or the GO term size. In the case of size, rrvgo will fetch the GO term size from the OrgDb object and use it as the score, thus favoring broader terms. Please note that scores are interpreted in the direction that higher are better, therefore if you use p-values as scores, minus log-transform them before.

NOTE:rrvgo uses the similarity between pairs of terms to compute a distance matrix, defined as (1-simMatrix). The terms are then hierarchically clustered using complete linkage, and the tree is cut at the desired threshold, picking the term with the highest score as the representative of each group.
Therefore, higher thresholds lead to fewer groups, and the threshold should be read as the minimum similarity between group representatives.

2.3 Plotting and interpretation

rrvgo provides several methods for plotting and interpreting the results.

2.3.1 Similarity matrix heatmap

Plot similarity matrix as a heatmap, with clustering of columns of rows turned on by default (thus arranging together similar terms).


The function internally uses pheatmap, and further parameters can be passed to this function.

2.3.2 Scatter plot depicting groups and distance between terms

Plot GO terms as scattered points. Distances between points represent the similarity between terms, and axes are the first 2 components of applying a PCoA to the (di)similarity matrix. Size of the point represents the provided scores or, in its absence, the number of genes the GO term contains.

scatterPlot(simMatrix, reducedTerms)