Analytics R 2: Analysis of high-throughput biological data in R, 2020/21

Analytics R is a two part, mandatory training session for DTP students.  Analytics R 1: Introduction to reproducible analyses in R will be held for first years.  When this cohort is in their second year, they will undertake Analytics R 2. This is the call out for current second year students to participate in Analytics R2.


The course will take place during from 21st April to 4th May 2021 during the following times and dates:

Wed 21 Apr Release set up instructions

Expected audience: Everyone

Fri 23 Apr 10:00-11:30 Office hours to assist with set up

Expected audience: Everyone

Mon 26 Apr 10:00-13:00 ‘Importing RNA-seq counts into R and quality assessment’  

Expected audience: Everyone

Wed 28 Apr 10:00-13:00 ‘Differential expression for RNA-seq

Expected audience: Everyone

Friday 30 Apr 10:00 -13:00 ‘Visualisation methods for RNA-seq data

Expected audience: Everyone

Tuesday 4 May 10:00-13:00 ‘Pathways and downstream analysis’

Expected audience: Everyone


The course will be delivered on-line. Links and joining instructions will be provided by the course tutor.

Who it is for

This course is mandatory for White Rose BBSRC DTP, CASE and White Rose Network (WRN) students who are in Year 1 (2020 starters).  NB Those students who were in Year 1 last year and did not complete all the essential modules of the course are required to complete these this year, in readiness for completing Analytics R2. These students have been alerted individually via email.

Course content

High-throughput sequencing is now established as a standard technique for many functional genomics studies; allowing the researcher to compare and contrast the transcriptomes of many individuals to obtain biological insight. A high-volume of data are generated from these experimental techniques and thus require robust and reproducible tools to be employed in the analysis.

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 well-respected DESEq2 analysis workflow. You will learn how to generate common plots for analysis and visualisation of gene expression data, such as boxplots and heatmaps. 

Although we will use RNA-seq data as a case study, the format of the data and methodologies will be highly applicable to other types of high-throughput omics data.


Students should have completed Analytics R1 in their first year. A basic understanding of the wet-lab techniques used in RNA-seq would be advantageous, but not required.

If you missed any of the Analytics R1 modules last year and have not received an email about this, please contact the DTP Co-ordinator, Catherine Liddle:

Learning outcomes

After this workshop the successful learner will have a practical understanding of:

  • Exploring RNA-seq count data and importing these data into R
  • Normalisation strategies for RNA-seq counts
  • Quality Assessment of counts
  • Identifying outliers, batch effects and sample mix-ups
  • Using the DESeq2 package to assess differential expression
  • Construction and interpretation of common visualisations
  • Using annotation packages to query biological databases
  • Methodology behind gene set testing and enrichment analysis


The course will be delivered by Mark Dunning of The University of Sheffield.


Not applicable for the on-line course.

Travel arrangements

Not applicable for the on-line course.

How to register

Please use the following link to register for this course:


If you have any queries or you feel you already have sufficient previous experience of a particular module, please discuss this directly with the tutor, Mark Dunning, email:

For general DTP queries, contact Catherine Liddle, White Rose BBSRC Doctoral Training Partnership Co-ordinator, e-mail: