- Maria Doyle <Maria.Doyle at petermac.org>
- Stefano Mangiola <mangiola.s at wehi.edu.au>
Material web page.
More details on the talk are below.
This is necessary in order to reproduce the code shown in the talk. The talk is designed for R 4.0 and can be installed using one of the two ways below.
Alternatively, you could install the talk using the commands below in R 4.0.
#install.packages('remotes')
# Need to set this to prevent installation erroring due to even tiny warnings, similar to here: https://github.com/r-lib/remotes/issues/403#issuecomment-748181946
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")
# Install talk package
remotes::install_github("stemangiola/monashMay5_tidytranscriptomics", build_vignettes = TRUE)
# To view vignettes
library(monashMay5tidytranscriptomics)
browseVignettes("monashMay5tidytranscriptomics")
To run the code, you could then copy and paste the code from the talk vignette or R markdown file into a new R Markdown file on your computer.
This talk will present how to perform analysis of 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 for RNA sequencing data with the tidySummarizedExperiment, tidybulk, tidyseurat, tidySingleCellExperiment, tidyseurat, tidyHeatmap and tidyverse packages. The tidybulk package provides a tidy data structure and a modular framework for bulk transcriptional analyses, the tidyseurat and tidySingleCellExperiment packages provide similar for single-cell, and tidyHeatmap provides a tidy implementation of ComplexHeatmap. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data.
- Basic knowledge of RStudio
- Some familiarity with tidyverse syntax
- Some familiarity with bulk RNA-seq and single cell RNA-seq
Recommended Background Reading Introduction to R for Biologists
- tidyverse
- tidybulk
- tidySummarizedExperiment
- tidySingleCellExperiment
- tidyseurat
- tidyHeatmap
- limma
- edgeR
- DESeq2
- airway
- org.Hs.eg.db
- dittoSeq
- ggrepel
- GGally
- plotly
In exploring and analysing RNA sequencing data, there are a number of key concepts, such as filtering, scaling, dimensionality reduction, hypothesis testing, clustering and visualisation, that need to be understood. These concepts can be intuitively explained to new users, however, (i) the use of a heterogeneous vocabulary and jargon by methodologies/algorithms/packages, (ii) the complexity of data wrangling, and (iii) the coding burden, impede effective learning of the statistics and biology underlying an informed RNA sequencing analysis.
The tidytranscriptomics approach to 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.
- To understand the key concepts and steps of RNA sequencing data analysis
- To approach data representation and analysis though a tidy data paradigm, integrating tidyverse with tidybulk, tidySingleCellExperiment and tidyHeatmap.
- Recall the key concepts of RNA sequencing data analysis
- Apply the concepts to publicly available data
- Create plots that summarise the information content of the data and analysis results