• 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>

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.

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.

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")

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

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

Recommended Background Reading Introduction to R for Biologists

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

• 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

First day

Second day

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.

• 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