Differential gene expression workshop using Salmon counts

Audience	Computational skills required	Duration
Biologists	Introduction to R	2-day workshop (~10 hours of trainer-led time)

Description

This repository has teaching materials for a 2-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.

NOTE: Materials in this repo are very similar to those presented in the DGE workshop. The slight difference is due to the use of 'pseudocounts' generated from transcriptome mapping, rather than raw counts obtained from the typical workflow.

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.

Lessons

Click here for links to lessons and the suggested schedule

Differential gene expression workshop

Audience	Computational Skills	Prerequisites	Duration
Biologists	Intermediate R	Introduction to R	2-day workshop (~12 hours of trainer-led time)

This repository has teaching materials for a 2-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.

Installation Requirements

Download the most recent versions of R and RStudio for your laptop:

• R (version 3.6.0 or above)
• RStudio

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.

(1) 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:

install.packages("insert_package_name_in_quotations")
install.packages("insert_package_name_in_quotations")
& so on ...

Note that these package names are case sensitive!

BiocManager
devtools
tidyverse
RColorBrewer
pheatmap
ggrepel
cowplot

(2) Install the below packages from Bioconductor. Load BiocManager, then run BiocManager's install() function 12 times for the 12 packages:

library(BiocManager)
install("insert_first_package_name_in_quotations")
install("insert_second_package_name_in_quotations")
& so on ...

Note that these package names are case sensitive!

DESeq2
clusterProfiler
DOSE
org.Hs.eg.db
pathview
DEGreport
tximport
AnnotationDbi
EnsDb.Hsapiens.v86
AnnotationHub
ensembldb

NOTE: The library used for the annotations associated with genes (here we are using org.Hs.eg.db) will change based on organism (e.g. if studying mouse, would need to install and load org.Mm.eg.db). The list of different organism packages are given here.

(3) Finally, please check that all the packages were installed successfully by loading them one at a time using the library() function.

library(DESeq2)
library(tidyverse)
library(RColorBrewer)
library(pheatmap)
library(ggrepel)
library(cowplot)
library(clusterProfiler)
library(DEGreport)
library(org.Hs.eg.db)
library(DOSE)
library(pathview)
library(tximport)
library(AnnotationDbi)
library(EnsDb.Hsapiens.v86)
library(AnnotationHub)
library(ensembldb)

(4) Once all packages have been loaded, run sessionInfo().

sessionInfo()

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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.