Coming from a background in Statistics and Applied Mathematics, Belinda Phipson completed her PhD in 2013 with Prof. Gordon Smyth and Prof. Peter Hall at The University of Melbourne and the Walter and Eliza Hall Institute of Medical Research. The focus of her PhD was on developing and improving empirical Bayes methods for analysing gene expression data. Her current research as a post doc in A/Prof. Alicia Oshlack’s group at the Murdoch Childrens Research Institute focuses on statistical methods for analysing RNA-Seq, single cell RNA-Seq and DNA methylation data.
Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include Unique Molecular Identifiers (UMIs), which tag individual molecules, and permits removal of PCR duplicates when estimating transcript abundances.
In this talk I will discuss the challenges of analysing single cell RNA-Seq data, including key differences between UMI and full-length single cell sequencing protocols, the abundance of genes with no signal in the data (zeroes) and batch effects. Practical advice on how to perform adequate cell and gene filtering will be covered. Finally, I will touch on normalisation methods currently available for single cell data, and how to simulate and evaluate analysis methods.
Presentation slides are not available from the speaker.