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Differential gene expression workshop

Audience Computational skills required Duration
Biologists Introduction to R 1.5-day workshop (~10 hours of trainer-led time)


This repository has teaching materials for a 1.5-day, hands-on Introduction to differential gene expression (DGE) analysis workshop. The workshop will lead participants through performing a differential gene expression analysis workflow on RNA-seq count data using R/RStudio. Working knowledge of R is required or completion of the Introduction to R workshop.

Learning Objectives

  • QC on count data using Principal Component Analysis (PCA) and hierarchical clustering
  • Using DESeq2 to obtain a list of significantly different genes
  • Visualizing expression patterns of differentially expressed genes
  • Performing functional analysis on gene lists with R-based tools

These materials are developed for a trainer-led workshop, but also amenable to self-guided learning.


Below are links to the lessons and suggested schedules:

Installation Requirements

  1. Download the most recent versions of R and RStudio for your laptop:
  1. Install the following packages using the instructions provided below.

NOTE:  When installing the following packages, if you are asked to select (a/s/n) or (y/n), please select “a” or "y" as applicable but know that it can take awhile.

(a) Install the below packages on your laptop from CRAN. You DO NOT have to go to the CRAN webpage; you can use the following function to install them one by one:

& so on ...

Packages to install from CRAN (note that these package names are case sensitive!):

  • BiocManager
  • RColorBrewer
  • pheatmap
  • ggrepel
  • devtools

(b) Install the below packages from Bioconductor, using BiocManager::install() function 7 times for the 7 packages:


Packages to install from Bioconductor (note that these package names are case sensitive!):

  • DESeq2
  • clusterProfiler
  • DOSE
  • pathview
  • DEGreport

(c) Use a new method of installation from GitHub to install the below packages using the following code:

  1. Finally, please check that all the packages were installed successfully by loading them one at a time using the library() function.
  1. Once all packages have been loaded, run sessionInfo().

These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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