May 11-12, 2016
9:00 am - 4:30 pm
Instructors: Belinda Phipson, Maria Doyle, Jovana Maksimovic, Harriet Dashnow, Matt Ritchie
Helpers: Jane Hawkey, Sepideh Foroutan, Ramyar Molania, Chenkai Ma, Luke Zappia, Katrina Bell, Anthony Hawkins, Enid Lam, Yih-Chih Chan, Matt Wakefield, Piyush Madhamshettiwar
In this workshop, you will be learning how to analyse RNA-seq count data, using R. This will include reading the data into R, quality control and performing differential expression analysis and gene set testing, with a focus on the limma-voom analysis workflow. You will learn how to generate common plots for analysis and visualisation of gene expression data, such as boxplots and heatmaps. You will also be learning how alignment and counting of raw RNA-seq data can be performed in R. In addition, you will be introduced to Degust, a user-friendly interactive tool for RNA-seq analysis. This workshop is aimed at biologists interested in learning how to perform differential expression analysis of RNA-seq data when reference genomes are available.
Who: The course is aimed at graduate students and other researchers. Some basic R knowledge is assumed. This is not an introduction to R course. If you are not familiar with the R statistical programming language we strongly encourage you to work through an introductory R course before you attend this workshop. We recommend the Software Carpentry R for Reproducible Scientific Analysis lessons up to and including vectorisation (topic 9).
Thank you to VLSCI for hosting this workshop.
Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). If you choose to bring your own RNAseq data, it must be count data (because we don't have the time or computational resources to map large data sets during the workshop). If you're not sure, here is an example of count data format.
Accessibility: We are committed to making this workshop accessible to everybody. The workshop organisers have checked that:
Contact: Please mail email@example.com for more information.
Note: this is a preliminary schedule. There may be changes to the timing and content.
If you have any trouble installing the software or packages, please arrive at 8:45am on the first day so we can help before the workshop starts.
In the first session of Day 1 we will cover some R skills that are particularly useful for RNAseq analysis, for example data frame manipulation, factors and subsetting using logical statements. If you are a more advanced R user, you may not need to attend this session.
|09:00||R for RNAseq|
|11:30||Introduction to RNA-seq theory|
|13:45||Differential expression continued|
|15:30||Differential expression continued|
|09:30||Alignment and feature counting|
|11:30||Gene set testing|
|13:45||Applying RNAseq (or BYO data)|
|15:30||Applying RNAseq (or BYO data) continued|
We will use this Etherpad for chatting, taking notes, and sharing URLs and bits of code.
To participate in this workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.
Even if you already have R installed, it is important that you have the latest version because some of the packages we will be using will not work with earlier versions of R. The latest version (available through the links below) is 3.2.5 for Windows and 3.2.4 for Mac 10.9 and above. For Mac 10.6-10.8 the latest version is 3.2.1 (available from here) and for that version you will also need to download this gfortran package gfortran package).
Software Carpentry maintains a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
You will also need to install the following R packages: limma, edgeR, gplots, org.Mm.eg.db, EDASeq, RColorBrewer, GO.db, BiasedUrn, DESeq2, Glimma, Rsubread.
These can all be obtained from Bioconductor, except for Glimma (see below).
Open RStudio and run the following commands to install packages from Bioconductor:
Repeat this for each package.
We will install Glimma from source (Mac users: Glimma_0.99.5.tgz, Windows: Glimma_0.99.5.zip). We can help with this during the workshop.
Mac and Linux users can install Rsubread using
Windows users will need to set up a GVL instance. Instructions to follow.