This tutorial will present how to perform analysis of single-cell 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 with the integration of packages present in the R CRAN and Bioconductor ecosystem, including tidyseurat, tidySingleCellExperiment, and tidyverse. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis.

Instructors:
Dr. Stefano Mangiola is a Postdoctoral researcher in the laboratory of Prof. Tony Papenfuss. His background spans from biotechnology to bioinformatics and biostatistics. His research focuses on prostate and breast tumour microenvironment, the development of statistical model for the analysis of RNA sequencing data, and data analysis and visualisation interfaces.
Dr. Maria Doyle is the Application and Training Specialist for Research Computing at the Peter MacCallum Cancer Centre in Melbourne, Australia. She has a PhD in Molecular Biology and currently works in bioinformatics and data science education and training. She is passionate about supporting researchers, reproducible research, open source and tidy data.

Recommended Prerequisites:
Basic knowledge of single cell transcriptomic analyses
Basic knowledge of tidyverse