Background knowledge for the Tidy Transcriptomics workshop

Maria Doyle, Peter MacCallum Cancer Centre1

Stefano Mangiola, Walter and Eliza Hall Institute2

Source: vignettes/background.Rmd

Schedule

Schedule can be found here.

Format: Hands on demos plus Q&A Interact: Zoom chat

What is transcriptomics?

“The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells”

Wikipedia

Why use transcriptomics?

  • Genome (DNA) pretty stable
  • Proteome (proteins) harder to measure
  • Transcriptome (RNA) can measure changes in expression of thousands of coding and non-coding transcripts

Possible experimental design

How does transcriptomics work?

Types of transcriptomic analyses

  • Differential expression
  • Cell type composition
  • Alternative splicing
  • Novel transcript discovery
  • Fusions identification
  • Variant analysis

    Topics in bold we will see in this workshop

Bulk RNA sequencing differential expression workflow

Differences between bulk and single-cell RNA sequencing

Shalek and Benson, 2017

Single-cell RNA sequencing analysis workflow

Tidy data and the tidyverse

This workshop demonstrates how to perform analysis of RNA sequencing data following the tidy data paradigm (Wickham and others 2014). 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. For more information, see the R for Data Science chapter on tidy data here.

[Tidy data for efficiency, reproducibilty and collaboration](https://www.openscapes.org/blog/2020/10/12/tidy-data/)

Tidy data for efficiency, reproducibilty and collaboration

The tidyverse is a collection of packages that can be used to tidy, manipulate and visualise data. We’ll use many functions from the tidyverse in this workshop, such as filter, select, mutate, pivot_longer and ggplot.

Adapted from [Getting Started with tidyverse in R](https://www.storybench.org/getting-started-with-tidyverse-in-r/)

Adapted from Getting Started with tidyverse in R

Getting started

If you want to follow along and run the code on your own computer, see instructions here. Alternatively, you can view the material at the workshop webpage here.

Wickham, Hadley, and others. 2014. “Tidy Data.” Journal of Statistical Software 59 (10): 1–23.

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