Statistical Methods for Single-Cell RNA-Seq Analysis (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section on Statistics in Genomics and Genetics
Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that were elusive just a few years ago. With the advantages of scRNA-seq come statistical challenges that are just beginning to be addressed. In this course, we will review the computational and statistical methods available for the design and analysis of scRNA-seq experiments including methods for quality control, normalization, accounting for technical noise, gene expression estimation and recovery, allele specific expression estimation, sub-population identification, pseudotemporal ordering and inference, and identification of differential distributions. Advantages and disadvantages of approaches in various settings will be discussed. Software demos will be also provided so that students will leave the course with the tools necessary to perform common analyses of single-cell RNA-seq data.
Instructor(s): Rhonda Bacher, University of Wisconsin - Madison; Christina Kendziorski, University of Wisconsin - Madison; Mingyao Li, University of Pennsylvania; Nancy Zhang, University of Pennsylvania
