Abstract

The goal of this tutorial is to apply bulk RNA deconvolution techniques to a problem with multiple variables - in this case, a model of diabetes is compared with its healthy counterparts. All you need to compare inferred cell compositions are well-annotated, high quality reference scRNA-seq datasets, transformed into MuSiC-friendly Expression Set objects, and your bulk RNA-samples of choice (also transformed into MuSiC-friendly Expression Set objects). For more information on how MuSiC works, you can check out their github site MuSiC or published article.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• How do the cell type distributions vary in bulk RNA samples across my variable of interest?
• For example, are beta cell proportions different in the pancreas data from diabetes and healthy patients?

Learning Objectives

• Apply the MuSiC deconvolution to samples and compare the cell type distributions
• Compare the results from analysing different types of input, for example, whether combining disease and healthy references or not yields better results