tidyomicsWorkshopBioc2023

Instructor names and contact information

• Stefano Mangiola <mangiola.s at wehi.edu.au>
• Michael Love

Syllabus

Material web page.

More details on the workshop are below.

Conference Galaxy platform

You can find the workshop on the BioC2023 Galaxy platform listed as:

• Package Demo: tidySingleCellExperiment showing tidy genomic and transcriptomic analyses, for a single-cell RNA-seq application

Click here for a full list of the BioC2023 workshop demos.

Workshop package installation

If you want to install the packages and material post-workshop, the instructions are below. The workshop is designed for R 4.3 and Bioconductor 3.17.

#install.packages('remotes')
          
# Install workshop package

remotes::install_github("tidyomics/tidyomicsWorkshopBioc2023", build_vignettes = TRUE)

# To view vignette
library(tidyomicsWorkshopBioc2023)
vignette("tidyGenomicsTranscriptomics")

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

This tutorial will present how to perform analysis of single-cell RNA sequencing 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 with the integration of packages present in the R CRAN and Bioconductor ecosystem, including tidyseurat, tidySingleCellExperiment and tidyverse. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis. For more information see the tidy transcriptomics blog.

In addition this workshop will finish with examples of how genomic and transcriptomic data can be combined, e.g. ChIP-seq and scRNA-seq, also using tidy data paradigms for genomic ranges. This is enabled with the plyranges package, with further information provided in the tidy-ranges-tutorial.

Pre-requisites

• Basic familiarity with single cell transcriptomic analyses
• Basic familiarity with tidyverse
• Basic familiarity with genomic ranges

Workshop Participation

The workshop format is a 45 minute session consisting of hands-on demos, exercises and Q&A.

R / Bioconductor packages used

• tidySingleCellExperiment
• plyranges

Workshop goals and objectives

The tidytranscriptomics approach to single-cell 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.

Learning goals

• To approach data representation and analysis though a tidy data paradigm, integrating tidyverse with the SingleCellExperiment data object.
• To explore integration of genomic and transcriptomic data also using a tidy data paradigm.

What you will learn

• Basic tidy operations possible with tidySingleCellExperiment and GRanges
• How to interface SingleCellExperiment with tidy manipulation and visualisation
• A real-world case study that will showcase the power of tidy single-cell methods compared with base/ad-hoc methods
• Examples of how to integrate genomic and transcriptomic data (ChIP-seq and RNA-seq)

What you will not learn

• The molecular technology of single-cell sequencing
• The fundamentals of single-cell data analysis
• The fundamentals of tidy data analysis
• Detailed data integration methods (multi-view or multi-omics algorithms)