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 DESeq2 analysis workflow. You will learn how to generate common plots for analysis and visualisation of gene expression data, such as boxplots and heatmaps.
This workshop is aimed at biologists interested in learning how to perform differential expression analysis of RNA-seq data when reference genomes are available.
There is a course Etherpad. Please post questions here and we will answer them as soon as we can (Or if you can answer someone elses question do so!). The trainers may also post useful code snippets here for you.
Day 1
9:30 - 10:15 - Introduction to RNAseq Methods - Ashley Sawle
10:15 - 11:00 - Introduction to Alignment and Quantification - Guillermo Parada Gonzalez
11:00 - 12:30 Practical: QC and Alignment with HISAT2
12:30 - 13:30 Lunch
13:30 - 17:30 Practical: Transcriptome assembly and quantificatioin with stringtie
Day 2
9:30 - 10:30 Normalisation; Quasi-mapping and quantification with Salmon - Guillermo Parada Gonzalez
10:30 - 12:30 Practical: Mapping and quantification with Star; Quantification with Salmon
12:30 - 13:30 - Lunch
13:30 - 14:00 - Introduction to RNAseq Analysis in R - Ashley Sawle
14:00 - 14:45 - RNA-seq Pre-processing - Ashley Sawle
14:45 - 17:30 - Linear Model and Statistics for Differential Expression - Oscar Rueda
Day 3
9:30 - 12:00 - Differential Expression for RNA-seq - Ashley Sawle
12:00 - 13:00 Lunch
13:00 - 15:30 Annotation and Visualisation of RNA-seq results - Abbi Edwards
15:30 - 17:30 Gene-set testing - Ashley Sawle
Some basic R knowledge is assumed (and is essential). Without it, you will struggle on this course. If you are not familiar with the R statistical programming language we strongly encourage you to work through an introductory R course before attempting these materials. We recommend reading our R crash course before attending, which should take around 1 hour
- You can of course start from a base R & Rstudio setup but you may find it easier to pull a Docker container image onto your Linux, Mac or Windows machine (You will need to install Docker and for Win & Mac we also recommend the Kitematic graphical interface to Docker. The image is pullable using 'docker pull mfernandes61/crukci_rnaseq_course' or searching for 'mfernandes61/crukci_rnaseq_course' in Kitematic.
The all of the lecture slides and other source materials, including R code and practical solutions, can be found in the course's Github repository
Introductory R materials:
Additional RNAseq materials:
Data: Example Mouse mammary data (fastq files): https://figshare.com/s/f5d63d8c265a05618137
Bioconductor help
Biostars
SEQanswers
This course is based on the course RNAseq analysis in R prepared by Combine Australia and delivered on May 11/12th 2016 in Carlton. We are extremely grateful to the authors for making their materials available; Maria Doyle, Belinda Phipson, Matt Ritchie, Anna Trigos, Harriet Dashnow, Charity Law.
