In this workshop we 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. We can achieve this for bulk RNA sequencing data with the tidybulk, tidyHeatmap and tidyverse packages. We will also touch on packages for tidy single-cell transcriptional analyses. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data. Recommended pre-requisites- Basic knowledge of RStudio- Some familiarity with tidyverse syntax- Background Reading Introduction to R for Biologists
Stefano Mangiola | Maria Doyle
Walter and Eliza Hall Institute | Peter MacCallum Cancer Centre
Tidy Transcriptomics
In this workshop we 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. We can achieve this for bulk RNA sequencing data with the tidybulk, tidyHeatmap and tidyverse packages. We will also touch on packages for tidy single-cell transcriptional analyses. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data. Recommended pre-requisites- Basic knowledge of RStudio- Some familiarity with tidyverse syntax- Background Reading Introduction to R for Biologists