Automatic Cell-Annotation for Single-Cell RNA-Seq Data Using a Reference Dataset - A SingleR Tutorial

Learn how to automatically annotate cell types in single-cell RNA-Seq data using SingleR and a reference dataset in this comprehensive tutorial video. Explore various cell annotation strategies, understand how SingleR works, and follow a step-by-step demonstration of the annotation process. Discover tips for choosing reference datasets, visualize results using UMAP plots, and evaluate cell type assignments through multiple diagnostic approaches. Gain practical insights into processing and analyzing single-cell RNA-Seq data for more effective cell type identification in your bioinformatics research.

Intro
Overview of cell annotation workflow
Strategies for automatic cell annotation
Marker-based annotation approach
Reference-based annotation approach
How does SingleR work?
Study design and goal of the analysis
Data used for demonstration
Reading data, filtering and pre-processing in Seurat
Pointers to choose reference dataset to run SingleR
Fetching reference data from celldex package
Run SingleR
Understanding singleR output
Visualize singleR labels in a UMAP plot
Annotation diagnostic 1: Based on scores within cells
Annotation diagnostic 2: Based on deltas across cells
Annotation diagnostic 3: Comparing cell type assignments to unsupervised clustering