This course is compiled automatically on 2022-01-24
The course is tested and available on MacOS, Windows and Ubuntu Linux for R version 4.1.2 (2021-11-01)
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.
R can be installed from the R-project website.
The R website can be found here http://www.r-project.org/.
The download links and associated installation instructions for multiple platforms can be found below provided by Revolution Analytics. https://cran.revolutionanalytics.com
We recommend installing R 4.1.2 as this is the version used to compile the course. Direct downloads for R 4.1.2 for the main platforms can be found below:
RStudio can be installed from the RStudio website.
RStudio can be downloaded for all platforms at the link below
https://rstudio.com/products/rstudio/download/
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.packages('BiocManager')
BiocManager::install('RockefellerUniversity/RU_RNAseq',subdir='rnaseq')
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('knitr')
BiocManager::install('testthat')
BiocManager::install('yaml')
This section introduces the first steps of the analysis of RNAseq data in Bioconductor. Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
In this section we will cover how to statistical test for statistically interesting genes in your RNAseq dataset. Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
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:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
In the fourth section we cover how to test for functionally interesting groups of genes in your RNAseq dataset. Session sections:
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
In this section we will cover how to statistical test for differential usage of isoforms in your RNAseq dataset.
The html slide presentation can be found at this link Slide
The single page html presentation can be found at this link Single Page
The code use in the presentations can be found at R code
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
If you would like contact us about general bioinformatics advice, support or collaboration, please contact us the Bioinformatics Resource Center at brc@rockefeller.edu.