Introduction to bulk and single-cell RNA sequencing analyses

zhejiang2020

Instructor names and contact information

  • Xueyi Dong <dong.x at wehi.edu.au>
  • Luyi Tian <tian.l at wehi.edu.au>
  • Hongke Peng <peng.h at wehi.edu.au>
  • Stefano Mangiola <mangiola.s at wehi.edu.au>

Syllabus

Material web page.

This material was created for the Zhejiang 2020 workshop workshop but it can also be used for self-learning.

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 4.0 and can be installed using one of the two ways below.

Via GitHub

You can install the workshop using the commands below in R 4.0.

# Install dependency manually

# Open R
Open R 4.0 or newer

# Install devtools if you do not have it
install.packages("devtools")

# Install the workshop package from Github
devtools::install_github("stemangiola/zhejiang2020", build_vignettes = TRUE)

# Load the workshop
library(zhejiang2020)

# List the vignettes, present in the vignettes directory
browseVignettes("zhejiang2020")

Workshop Description

This workshop will present how to perform analysis of bulk and single-cell RNA sequencing count data following base R paradigm. Example of the use of tidy paradigm is given at the end of each section.

The bulk analyses were based on the Bioconductor workflow package RNAseq123 and the workshop for tidy transcriptomics BioC Asia 2020

Pre-requisites

  • Basic knowledge of RStudio
  • Familiarity with R base and tidyverse syntax

Recommended Background Reading Introduction to R for Biologists

Workshop Participation

The workshop format is 2 days, 2 hours sessions each day consisting of hands-on demos with Q&A.

R / Bioconductor packages used

  • dittoSeq
  • dplyr
  • edgeR
  • ggplot2
  • ggrepel
  • Glimma
  • gplots
  • igraph
  • limma
  • Mus.musculus
  • purrr
  • R.utils
  • RColorBrewer
  • readr
  • RNAseq123
  • scater
  • scran
  • SingleCellExperiment
  • SingleR
  • stats
  • stringr
  • SummarizedExperiment
  • tibble
  • tidybulk
  • tidyr
  • tidySingleCellExperiment
  • utils

Time outline

First day

Activity Time
Bulk RNA sequencing analyses 1h 20m
Questions 20m
Break 30m
Tidy bulk RNA sequencing analyses 30m
Questions 20m

Second day

Activity Time
Single-cell RNA sequencing analyses 1h 20m
Questions 20m
Break 30m
Tidy single-cell RNA sequencing analyses 30m
Questions 20m

Workshop goals and objectives

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.

Learning goals

  • To understand the key concepts and steps of RNA sequencing count data analysis
  • Apply the concepts to publicly available data
  • Create plots that summarise the information content of the data and analysis results
  • To approach critical thinking