Instructor names and contact information

  • Maria Doyle <Maria.Doyle at>
  • Stefano Mangiola <mangiola.s at>


Material web page.

More details on the workshop are below.

Workshop package installation

This is necessary in order to reproduce the code shown in the workshop. The workshop is designed for R 4.1 and can be installed using one of the two ways below.

Via Docker image

If you’re familiar with Docker you could use the Docker image which has all the software pre-configured to the correct versions.

docker run -e PASSWORD=abc -p 8787:8787 stemangiola/bioc2021_tidytranscriptomics:bioc2021

Once running, navigate to http://localhost:8787/ and then login with Username:rstudio and Password:abc.

You should see the Rmarkdown file with all the workshop code which you can run.

Via GitHub

Alternatively, you could install the workshop using the commands below in R 4.1.


# Need to set this to prevent installation erroring due to even tiny warnings, similar to here:

# Install same versions used in the workshop
remotes::install_github(c("stemangiola/tidybulk@v1.4.0"", "stemangiola/tidySummarizedExperiment@v1.2.0", "stemangiola/tidySingleCellExperiment@v1.3.0"))

# Install workshop package
remotes::install_github("stemangiola/bioc2021_tidytranscriptomics", build_vignettes = TRUE)

# To view vignettes

To run the code, you could then copy and paste the code from the workshop vignette or R markdown file into a new R Markdown file on your computer.

Workshop Description

Recently, plyranges and tidybulk have made efforts toward the harmonization of biological data structures and workflows using the concept of data tidiness, to facilitate modularisation. In this workshop, we present tidySingleCellExperiment and tidySummarizedExperiment, two R packages that allow the user to visualise and manipulate SingleCellExperiment and SummarizedExperiment objects in a tidy fashion. Importantly, the tidybulk framework now works natively with SummarizedExperiment objects and, thanks to tidySummarizedExperiment, allows tidy and modular RNA sequencing analyses without renouncing the efficiency of Bioconductor data containers. These tools are part of the tidytranscriptomics R software suite, and represent an effort toward the harmonisation of transcriptional analyses under the tidy umbrella.


  • Some familiarity with tidyverse syntax
  • Some familiarity with bulk RNA-seq and single cell RNA-seq

Recommended Background Reading Introduction to R for Biologists

Workshop Participation

The workshop format is a 1.5 hour session consisting of hands-on demos, challenges and Q&A.

R / Bioconductor packages used

  • tidySummarizedExperiment
  • tidySingleCellExperiment
  • tidybulk
  • tidyHeatmap
  • limma
  • edgeR
  • DESeq2
  • airway
  • dittoSeq
  • ggrepel
  • GGally
  • plotly

Time outline


Activity - Hands on demos with Q&A Time
Part 1 Bulk RNA-seq with tidySummarizedExperiment and tidybulk 45
Part 2 Single-cell RNA-seq with tidySingleCellExperiment 45
Total 90m

Learning goals

  • To understand how transcriptomic data can be represented and analysed according to the tidy data paradigm with tidySummarizedExperiment, tidybulk and tidySingleCellExperiment.

Learning objectives

  • Explore, visualise and analyse bulk RNA-seq count data with tidySummarizedExperiment and tidybulk
  • Explore and visualise single cell RNA-seq count data with tidySingleCellExperiment