## Overview

This workshop will focus on performing gene-set enrichment analysis of transcriptomic data and visualising the results of enrichment analysis. We will perform single-sample gene-set enrichment using methods in the singscore package to explore molecular phenotypes in individual samples. Following this, we will perform gene-set enrichment analysis using tools from the limma and edgeR packages. Finally, we will demonstrate a graph-based approach to visualise, summarise and interpret resutls of gene-set enrichment analysis.

The workshop will be organised into two broad sections:

• Molecular phenotyping of individual samples
• Identifying and visualising higher-order phenotypes

Detailed material can be found here.

## Pre-requisites

The course is aimed at PhD students, Master’s students, and third & fourth year undergraduate students. Some basic R knowledge is assumed - this is not an introduction to R course. If you are not familiar with the R statistical programming language it is compulsory that you work through an introductory R course before you attend this workshop.

## R packages used

The following key R packages will be used:

• singscore
• vissE
• msigdb
• emtdata
• edgeR
• limma
• GSEABase
• igraph

## Time outline

Activity Time
Introduction & setup 10m
Part 1. Molecular phenotyping of individual samples 45m
Part 2. Identifying and visualising higher-order phenotypes 45m
Q & A 10m

## Workshop goals and objectives

### Learning goals

• Learn how to perform gene-set testing in R.
• Understand the results of gene-set enrichment analysis.
• Understand the importance of visualisation in bioinformatics and computational biology.

### Learning objectives

• Perform a gene-set enrichment analysis and interpret the results.
• Apply vissE to identify higher-order phenotypes and to visualise the results of any gene-set enrichment analysis.

## Workshop package installation

### Guide

This is necessary in order to reproduce the code shown in the workshop. The workshop is designed for R 4.1 and can be installed using one of the two ways below.

### Via Docker image

If you’re familiar with Docker you could use the Docker image which has all the software pre-configured to the correct versions.

docker run -e PASSWORD=password -p 8787:8787 bhuvad/genesetanalysisworkflow:latest

Once running, navigate to http://localhost:8787/ and then login with Username:rstudio and Password:password.

You should see the Rmarkdown file with all the workshop code which you can run.

### Via GitHub

Alternatively, you could install the workshop using the commands below in R 4.1.

install.packages('remotes')

# Install workshop package
remotes::install_github("DavisLaboratory/GenesetAnalysisWorkflow", build_vignettes = TRUE)

# To view vignettes
library(GenesetAnalysisWorkflow)
browseVignettes("GenesetAnalysisWorkflow")