1 Licensing

The enrichViewNet package and the underlying enrichViewNet code are distributed under the Artistic license 2.0. You are free to use and redistribute this software.

2 Citing

If you use this package for a publication, we would ask you to cite the following:

Deschênes A, Belleau P, Faure RL, Fernandes MJ, Krasnitz A, Tuveson DA (2023). enrichViewNet: From functional enrichment results to biological networks. doi:10.18129/B9.bioc.enrichViewNet, https://bioconductor.org/packages/enrichViewNet.

3 Introduction

High-throughput technologies are routinely used in basic and applied research and are key drivers of scientific discovery. A major challenge in using these experimental approaches is the analysis of the large amount of data generated. These include lists of proteins or genes generated by mass spectrometry, single-cell RNA sequencing and/or microarray analysis, respectively. There is thus a need for robust bioinformatic and statistical tools that can analyze these large datasets and display the data in the form of networks that illustrate the biological and conceptual links with findings in the literature. This gap has been partially addressed by several bioinformatic tools that perform enrichment analysis of the data and/or present it in the form of networks.

Functional enrichment analysis tools, such as Enrichr (Kuleshov et al. 2016) and DAVID (Dennis et al. 2003), are specialized in positioning novel findings against well curated data sources of biological processes and pathways. Most specifically, those tools identify functional gene sets that are statistically over- (or under-) represented in a gene list (functional enrichment). The traditional output of a significant enrichment analysis tool is a table containing the significant gene sets with their associated statistics. While those results are extremely useful, their interpretation is challenging. The visual representation of these results as a network can greatly facilitate the interpretation of the data.

Biological network models are visual representations of various biological interacting elements which are based on mathematical graphs. In those networks, the biological elements are generally represented by nodes while the interactions and relationships are represented by edges. One of the widely used network tools in the quantitative biology community is the open source software Cytoscape (Shannon et al. 2003). In addition of biological data visualization and network analysis, Cytoscape can be expended through the use of specialized plug-ins such as BiNGO that calculates over-represented GO terms in a network (Maere, Heymans, and Kuiper 2005) or CentiScaPe that identifies relevant network nodes (Scardoni, Petterlini, and Laudanna 2009).

The g:Profiler enrichment analysis tool (Raudvere et al. 2019) is web based and has the particularity of being accompanied by the CRAN package gprofiler2 (Kolberg et al. 2020). The gprofiler2 package gives the opportunity to researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. This greatly facilitates research reproducibility.

The enrichViewNet package enables the visualization of functional enrichment results as network graphs. Visualization of enriched terms aims to facilitate the analyses of complex results. Compared to popular enrichment visualization graphs such as bar plots and dot plots, network graphs unveil the connection between the terms as significant terms often share one or multiple genes. Moreover, the enrichViewNet package takes advantage of a powerful network visualization tool which is Cytoscape. By doing so, all the functionalities of this mature software can be used to personalize and analyze the enrichment networks.

First, the enrichViewNet package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network (Shannon et al. 2003). In the biological networks generated by enrichViewNet, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). Only genes present in the query used for the enrichment analysis are shown.

Figure 1: A network where significant GO terms and genes are presented as nodes while edges connect each gene to its associated term(s)

The enrichViewNet package offers the option to generate a network for only a portion of the significant terms by selecting the source or by providing a specific list of terms.Once the network is created, the user can personalize the visual attributes and integrate external information such as expression profiles, phenotypes and other molecular states. The user can also perform network analysis.

In addition, the enrichViewNet package also provides the option to create enrichment maps from functional enrichment results. The enrichment maps have been introduced in the Bioconductor enrichplot package (Yu 2022). Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes. Thus, enriched terms with overlapping genes cluster together. This type of graphs facilitate the identification of functional modules.

Figure 2: An enrichment map using significant Kegg terms where edges are connecting terms with overlapping genes

enrichViewNet has been submitted to Bioconductor to aid researchers in carrying out reproducible network analyses using functional enrichment results.

4 Installation

To install this package from Bioconductor, start R (version 4.3 or later) and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

BiocManager::install("enrichViewNet")

5 General workflow

The following workflow gives an overview of the capabilities of enrichViewNet:

From functional enrichment results to biological networks

29 October 2024

Contents

1 Licensing

2 Citing

3 Introduction

4 Installation

5 General workflow