THIS PAGE IS STILL UNDER CONSTRUCTION
No lecture slides for this class. We’ll spend the entire time working on scRNA-seq script.
Overview
In the last lecture and lab, you learned and practiced the fundamental steps of single cell data analysis in R including reading and filtering data, performing principal component and UMAP dimensional reduction, clustering, integration of samples, and cell annotation. In this lecture, you will expand your toolbox of packages to perform functional analyses of single cell data to gain biological insights. We will cover pseudotime and velocity analyses, cell-cell communication inference, and methods and tools to perform gene set enrichment / gene set scoring on single cells. Collectively these tools provide a strong founation for exploratory and targeting investigation of any single cell dataset.
Learning Objectives
- Understand the concepts of pseudotime and velocity, their uses and limitations
- Apply pseudotime analyses to a dataset
- Familiarity with underlying concepts and limitations of cell-cell communication inference strategies
- Infer cell-cell receptor/ligand interactions in a single cell dataset
- Compare and contrast differen gene set enrichment and gene set scoring methods available
- Perform gene set enrichment on a single cell data set
- Visualize and contrast gene set signatures across samples and conditions
What you need
You will need to download the Seurat object which contains data from XXXXXX. These data are courtesy of XXXXXX. We should think about what datasets to use. The Hunter data set used for integration would probably be good for cell cell communication and for enrichment. We would need a different dataset for for pseudotime, ideally not the intestinal dataset if we expect them to perform pseudotime on those cells during the lab.