Abstract

Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq.

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

• What are the steps to prepare single-cell RNA-Seq data for clustering?
• How to cluster cells in single-cell RNA-Seq data?
• How cell type annotation can be assigned to cell clusters?

Learning Objectives

• Describe an AnnData object to store single-cell data
• Explain the preprocessing steps for single-cell data
• Evaluate quality of single-cell data and apply steps to select and filter cells and genes based on QC
• Execute data normalization and scaling
• Identify highly variable genes
• Construct and run a dimensionality reduction using Principal Component Analysis
• Perform a graph-based clustering for cells
• Identify marker genes for the clusters
• Construct and run a cell type annotation for the clusters