Contents

1 Introduction

Geneset enrichment is an important step in biological data analysis workflows, particularly in bioinformatics and computational biology. At a basic level, one is performing a hypergeometric or Kol-mogorov–Smirnov test to determine if a group of genes is over-represented or enriched, respectively, in pre-defined sets of genes, which suggests some biological relevance. The R package hypeR brings a fresh take to geneset enrichment, focusing on the analysis, visualization, and reporting of enriched genesets. While similar tools exists - such as Enrichr (Kuleshov et al., 2016), fgsea (Sergushichev, 2016), and clusterProfiler (Wang et al., 2012), among others - hypeR excels in the downstream analysis of gene-set enrichment workflows – in addition to sometimes overlooked upstream analysis methods such as allowing for a flexible back-ground population size or reducing genesets to a background distribution of genes. Finding relevant biological meaning from a large number of often obscurely labeled genesets may be challenging for researchers. hypeR overcomes this barrier by incorporating hierarchical ontologies - also referred to as relational genesets - into its workflows, allowing researchers to visualize and summarize their data at varying levels of biological resolution. All analysis methods are compatible with hypeR’s markdown features, enabling concise and reproducible reports easily shareable with collaborators. Additionally, users can import custom genesets that are easily defined, extending the analysis of genes to other areas of interest such as proteins, microbes, metabolites, etc. The hypeR package goes beyond performing basic enrichment, by providing a suite of methods designed to make routine geneset enrichment seamless for scientists working in R.

2 Documentation

Please visit https://montilab.github.io/hypeR-docs/ for documentation, examples, and demos for all features and usage or read our recent paper hypeR: An R Package for Geneset Enrichment Workflows published in Bioinformatics.

3 Installation

hypeR currently requires the latest version of R (>= 3.6.0) to be installed directly from Github or Bioconductor. To install with R (>= 3.5.0) see below. Use with R (< 3.5.0) is not recommended.

Install the development version of the package from Github.

devtools::install_github("montilab/hypeR")

Or install the development version of the package from Bioconductor.

BiocManager::install("montilab/hypeR", version="devel")

Or install with Conda.

conda create --name hyper
source activate hyper
conda install -c r r-devtools
R
library(devtools)
devtools::install_github("montilab/hypeR")

Or install with previous versions of R.

git clone https://github.com/montilab/hypeR
nano hypeR/DESCRIPTION
# Change Line 8
# Depends: R (>= 3.6.0) -> Depends: R (>= 3.5.0)
R
install.packages("path/to/hypeR", repos=NULL, type="source")

Load the package into an R session.

library(hypeR)

4 Basics

4.1 Terminology

All analyses with hypeR must include one or more signatures and genesets.

4.1.1 Signature

There are multiple types of enrichment analyses (e.g. hypergeometric, kstest, gsea) one can perform. Depending on the type, different kinds of signatures are expected. There are three types of signatures hypeR() expects.

# Simply a character vector of symbols (hypergeometric)
signature <- c("GENE1", "GENE2", "GENE3")

# A ranked character vector of symbols (kstest)
ranked.signature <- c("GENE2", "GENE1", "GENE3")

# A ranked named numerical vector of symbols with ranking weights (gsea)
weighted.signature <- c("GENE2"=1.22, "GENE1"=0.94, "GENE3"=0.77)

4.1.2 Geneset

A geneset is simply a list of vectors, therefore, one can use any custom geneset in their analyses, as long as it’s appropriately defined. Additionally, hypeR() recognized object oriented genesets called gsets and rgsets objects, which are explained later in the documentation.

genesets <- list("GSET1" = c("GENE1", "GENE2", "GENE3"),
                 "GSET2" = c("GENE4", "GENE5", "GENE6"),
                 "GSET3" = c("GENE7", "GENE8", "GENE9"))

4.2 Usage

4.2.1 Example Data

In these tutorials, we will use example data. The example data includes pre-computed results from common gene expression analysis workflows such as diffential expression and weighted gene co-expression.

hypdat <- readRDS(file.path(system.file("extdata", package="hypeR"), "hypdat.rds"))

Using a differential expression dataframe created with Limma, we will extract a signature of upregulated genes for use with a hypergeometric test and rank genes descending by their differential expression level for use with a kstest.

limma <- hypdat$limma
reactable(limma)