Abstract

You’ve previously done all the work to make a single cell matrix. Now it’s time to fully process our data using Seurat: remove low quality cells, reduce the many dimensions of data that make it difficult to work with, and ultimately try to define clusters and find some biological meaning and insights! There are many packages for analysing single cell data - Seurat, Scanpy, Monocle, Scater, and many more. We’re working with Seurat because it is well updated, broadly used, and highly trusted within the field of bioinformatics.

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

• Is my single cell dataset a quality dataset?
• How do I pick thresholds and parameters in my analysis? What’s a “reasonable” number, and will the world collapse if I pick the wrong one?
• How do I generate and annotate cell clusters?

Learning Objectives

• Interpret quality control plots to direct parameter decisions
• Repeat analysis from matrix to clustering to labelling clusters
• Identify decision-making points
• Appraise data outputs and decisions
• Explain why single cell analysis is an iterative process (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly)