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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:
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 loadorg.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()
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.