- Ash Sawle - Bioinformatics Core, Cancer Research UK Cambridge Institute
- Chandra Chilamakuri - Bioinformatics Core, Cancer Research UK Cambridge Institute
- Erin Doody - Sainsbury Laboratory, University of Cambridge
- Ian Tsang - NIAB, Cambridge
- Puspendu Sardar - Department of Medicine, University of Cambridge
In this workshop, you will be learning how to analyse RNA-seq data. This will include read alignment, quality control, quantification against a reference, reading the count data into R, 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.
Whilst we have run this course for several years, we are still improving and learning how to
teach it best.
Please bear with us if there are any technical hitches, and
be aware that timings for different sections laid out in the schedule below may
not be adhered to. There may be some necessity to make adjustments to the course
as we go.
-
Basic experience of using a UNIX/LINUX command line is assumed
- Watch this overview video (and accompanying materials up to section 6).
-
Some R knowledge is assumed and 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 our Introduction to R course
Trainers: Ash, Chandra, Erin, Ian, Puspendu
9:30 - 9:45 - Welcome - in person
9:45 - 10:15 - Introduction to RNAseq Methods - Erin
10:15 - 11:00 Raw read file format and QC - Ian
11:00 - 13:30 Alignment and Quantification of Gene Expression with Salmon - Chandra
13:30 - 14:30 Lunch
14:30 - 15:30 QC of alignment - Chandra
15.30 - 17.30 Data Exploration in R (pdf) - Ash
Trainers: Ash, Chandra, Erin, Ian, Puspendu
9:30 - 10:15 Introduction to RNAseq Analysis in R - Ash
10:15 - 13:00 Statistical Analysis of Bulk RNAseq Data - Ash
- Part I: Statistics of RNA-seq analysis
- Part II: Linear Models in R and DESeq2 (pdf)
- Slides
- Find the worksheet in
Course_Materials/stats/models_in_r_worksheet.R
13:00 - 14:00 Lunch
14:00 - 17:30 - Differential Expression for RNA-seq (pdf) - Chandra
Trainers: Ash, Chandra, Erin, Ian, Puspendu
9.30 - 9.45 - Recap of Day 1 and 2 - Chandra
9.45 - 12.30 Annotation and Visualisation of RNA-seq results - Ian / Chandra
12.30 - 13.30 Lunch
13.30 - 16:30 Gene-set testing - Ash
The lecture slides and other source materials, including R code and practical solutions, can be found in the course's Github repository
The full data used in the course can be downloaded from dropbox.
Instructions to install software are available from the "Software installation instructions" page.
The Extended Materials contain extensions to some of the sessions and additional materials, including instruction on downloading and processing the raw data for this course, a link to an excellent R course, and where to get further help after the course.
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.
The materials have been rewritten/modified/corrected/updated by various contributors over the past 5 years including:
Abigail Edwards Ashley D Sawle Chandra Chilamakuri Dominique-Laurent Couturier Guillermo Parada González Hugo Tavares Jon Price Mark Dunning Mark Fernandes Oscar Rueda Sankari Nagarajan Stephane Ballereau Tom Smith Zeynep Kalender Atak
Apologies if we have missed anyone!