Material web page.
This material was created for the BioC Asia 2020 conference workshop but it can also be used for self-learning.
More details on the workshop are below.
Update: 23rd Oct 2020. The tidySCE package has been renamed to tidySingleCellExperiment.
This is necessary in order to reproduce the code shown in the workshop. The workshop is designed for R
4.0 and can be installed using one of the two ways below.
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/biocasia2020_tidytranscriptomics:biocasia2020
Once running, navigate to http://localhost:8787/ and then login with
You should see the Rmarkdown file with all the workshop code which you can run.
Alternatively, you could install the workshop using the commands below in R
# Install same versions used in the workshop #install.packages('remotes') remotes::install_github(c("firstname.lastname@example.org", "stemangiola/tidySCE@v0.99.2", "stemangiola/tidyHeatmap@v1.1.5")) # Install workshop package remotes::install_github("stemangiola/biocasia2020_tidytranscriptomics", build_vignettes = TRUE) # To view vignettes library(biocasia2020tidytranscriptomics) browseVignettes("biocasia2020tidytranscriptomics")
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.
This workshop will present how to perform analysis of RNA sequencing count data following the tidy data paradigm. The tidy data paradigm provides a standard way to organise data values within a dataset, where each variable is a column, each observation is a row, and data is manipulated using an easy-to-understand vocabulary. Most importantly, the data structure remains consistent across manipulation and analysis functions.
This can be achieved for RNA sequencing count data with the tidybulk, tidySCE, tidyHeatmap and tidyverse packages. The tidybulk package provides a tidy data structure and a modular framework for bulk transcriptional analyses, the tidySCE package provides similar for single-cell transcriptional analyses, and tidyHeatmap provides a tidy implementation of ComplexHeatmap. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data.
Recommended Background Reading Introduction to R for Biologists
The workshop format is a 55 min session consisting of hands-on demos with Q&A.
|Part 1 Bulk RNA-seq with tidybulk||25m|
|Part 2 Single-cell RNA-seq with tidySCE||30m|
In exploring and analysing RNA sequencing count data, there are a number of key concepts, such as filtering, scaling, dimensionality reduction, hypothesis testing, clustering and visualisation, that need to be understood. These concepts can be intuitively explained to new users, however, (i) the use of a heterogeneous vocabulary and jargon by methodologies/algorithms/packages, (ii) the complexity of data wrangling, and (iii) the coding burden, impede effective learning of the statistics and biology underlying an informed RNA sequencing analysis.
The tidytranscriptomics approach to RNA sequencing data analysis abstracts out the coding-related complexity and provides tools that use an intuitive and jargon-free vocabulary, enabling focus on the statistical and biological challenges.