Course Overview

Course Integrity

This course is compiled automatically on 2025-05-11

The course is tested and available on MacOS, Windows and Ubuntu Linux for R version 4.4.0 (2024-04-24)

Previous versions of the course can be found here



Overview

This course introduces RNAseq analysis in Bioconductor.

The course consists of 4 sections. This walk you through each step of a normal RNAseq analysis workflow. It covers alignment and counting, looking for significant changes in expression, testing for enrichment in groups of genes and checking for changes in isoform usage. Exercises and answer sheets are included after all subsections to practice techniques and provide future reference examples.

Course material and exercises are available to view as rendered HTML at https://rockefelleruniversity.github.io/RU_RNAseq/. All material is available to download under GPL v2 license.



Setting up


System Requirements

Install IGV

IGV can be installed from the BROAD website.

https://www.broadinstitute.org/igv/


Install R

R can be installed from the R-project website.

The R website can be found here http://www.r-project.org/. This website has all the latest information about R updates, conferences and installation

You can use this direct links to the install for each major OS:


Install RStudio

RStudio can be installed from the RStudio website.

http://www.rstudio.com/

RStudio can be downloaded for all platforms at the link below

https://rstudio.com/products/rstudio/download/


Install Salmon

There is no R package for Salmon, but Salmon is available in the Anaconda package repository for Linux or MacOS. The easiest way to install Salmon is using the R package Herper. Herper allows you to manage and install Anaconda packages from within R.

BiocManager::install("Herper")
library(Herper)

Once Herper is installed you can install Salmon with the install_CondaTools function. Behind the scenes, Herper will install the most minimal version of conda (called miniconda), and then will create a new environment into which Salmon will be installed. When you run the function it prints out where Salmon is installed.

The env argument is the name you want to give the environment created. The pathToMiniConda specifies the location you want to install Miniconda, and all the conda tools like Salmon.

install_CondaTools(tools="salmon", env="RNAseq_analysis", pathToMiniConda="/path/to/install")

More information on Salmon is available from the Combine-lab github page and instructions for other installation methods can be found on their page at the link here here.


Install required R packages

R Packages can be installed from the course package or from CRAN/Bioconductor. These commands should be written into the R console. Once R and RStudio is installed, you can copy and paste these install commands into lower left pane of RStudio which should be labelled “Console”. If you run into any errors, do this one line at a time.

From the course package
install.packages('BiocManager')
install.packages('remotes')
BiocManager::install('RockefellerUniversity/RU_RNAseq',subdir='rnaseq')
From CRAN and Bioconductor
install.packages('BiocManager')
BiocManager::install('methods')
BiocManager::install('ggplot2')
BiocManager::install('goseq')
BiocManager::install('rmarkdown')
BiocManager::install('org.Mm.eg.db')
BiocManager::install('DESeq2')
BiocManager::install('apeglm')
BiocManager::install('tximport')
BiocManager::install('ShortRead')
BiocManager::install('BSgenome.Mmusculus.UCSC.mm10')
BiocManager::install('TxDb.Mmusculus.UCSC.mm10.knownGene')
BiocManager::install('Rsubread')
BiocManager::install('Rsamtools')
BiocManager::install('GenomicAlignments')
BiocManager::install('TxDb.Hsapiens.UCSC.hg19.knownGene')
BiocManager::install('GenomicFeatures')
BiocManager::install('GSEABase')
BiocManager::install('fgsea')
BiocManager::install('DEXSeq')
BiocManager::install('limma')
BiocManager::install('Herper')
BiocManager::install('msigdbr')
BiocManager::install('RColorBrewer')
BiocManager::install('vsn')
BiocManager::install('clusterProfiler')
BiocManager::install('NbClust')
BiocManager::install('pheatmap')
BiocManager::install('enrichplot')
BiocManager::install('Rfastp')
BiocManager::install('ggnewscale')
BiocManager::install('eulerr')
BiocManager::install('pcaExplorer')
BiocManager::install('rio')
BiocManager::install('EnhancedVolcano')
BiocManager::install('knitr')
BiocManager::install('testthat')
BiocManager::install('yaml')

Download the material

Download the material




The Presentations


RNAseq, Session 1

This section introduces the first steps of the analysis of RNAseq data in Bioconductor. Session sections:

  • Preprocessing RNAseq data in R
  • Alignment of data
  • Quantification of gene, exon and transcript expression levels

RNAseq, Session 2

In this section we will cover how to statistical test for statistically interesting genes in your RNAseq dataset. Session sections:

  • Import of transcript quantification into gene level summaries
  • Differential gene expression analysis of RNAseq data in R

RNAseq, Session 3

In this section we cover how to test work with complex datasets and how to start visualizing different aspects of your datasets i.e. PCA and clustering.

Session sections:

  • Differential analysis with multiple groups.
  • PCA
  • Clustering

RNAseq, Session 4

In the fourth section we cover how to test for functionally interesting groups of genes in your RNAseq dataset. Session sections:

  • Working with MSigDB gene set collection in R
  • Gene set enrichment using goseq
  • Gene set enrichment using clusterProfiler

RNAseq, Session 5

In this section we will cover how to statistical test for differential usage of isoforms in your RNAseq dataset.

  • Differential exon usage
  • Plotting DEU for transcripts

Getting help


Course help

For advice, help and comments for the material covered in this course please contact us at the issues page associated to this course.

The link to the help pages can be found here


General Bioinformatics support

If you would like contact us about general bioinformatics advice, support or collaboration, please contact us the Bioinformatics Resource Center at .