In stemangiola/rladiestunis2021_tidytranscriptomics: Introduction to Tidy Transcriptomics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

knitr::include_graphics("../inst/vignettes/tidybulk_logo.png")

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

knitr::include_graphics("../inst/vignettes/transcriptomics.jpg")

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

knitr::include_graphics("../inst/vignettes/ScreenShot2.png")

How does transcriptomics work?

knitr::include_graphics("../inst/vignettes/ScreenShot3.png")

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

knitr::include_graphics("../inst/vignettes/bulk_RNAseq_pipeline.png")

Differences between bulk and single-cell RNA sequencing

knitr::include_graphics("../inst/vignettes/bulk_vs_single.jpg")

Shalek and Benson, 2017

Single-cell RNA sequencing analysis workflow

knitr::include_graphics("../inst/vignettes/single_cell_RNAseq_pipeline.png")

Tidy data and the tidyverse

This workshop demonstrates how to perform analysis of RNA sequencing data following the tidy data paradigm [@wickham2014tidy]. 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.

knitr::include_graphics("../inst/vignettes/tidydata_1.jpg")

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.

knitr::include_graphics("../inst/vignettes/tidyverse.png")

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.

stemangiola/rladiestunis2021_tidytranscriptomics documentation built on Dec. 23, 2021, 5:30 a.m.