Abstract

You've done all the work to make a single cell matrix, with mitochondrial genes flagge and buckets of cell metadata from all your variables of interest. Now it's time to fully process our data, to remove low quality cells, to reduce the many dimensions of the data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights! There are many packages for analysing single cell data - Seurat, Scanpy, Monocle, Scater, and so forth. We're working with Scanpy, although Galaxy has training using other packages, which you can explore on our {% icon level %} Single-cell training topic.

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 can I assess my dataset quality?
• 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 inform parameter-decision making
• Repeat analysis from matrix to clustering
• Identify decision-making points
• Appraise data outputs and make informed decisions
• Explain why single cell analysis is an iterative (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly) process