Automatic cell-annotation for single-cell RNA-Seq data: A detailed SingleR tutorial (PART 1)
One of the most challenging task in processing single-cell RNA-Seq data is to annotate cell types. In this video I walk through what is a typical cell annotation workflow, discuss various annotation strategies and their strengths and pitfalls. Further, I explain how #SingleR works and demonstrate how to annotate cell types using SingleR using a single reference dataset. Lastly, I discuss various diagnostics to evaluate cell type assignments by singleR and visualize cell labels as a UMAP.
I hope you find this video helpful! Leave your thoughts in the comment section below!
▸ Link to Data:
https://www.10xgenomics.com/resources...
▸ Link to Code:
https://github.com/kpatel427/YouTubeT...
▸ Resources/Vignettes:
1. https://www.nature.com/articles/s4159...
2. http://bioconductor.org/books/release...
3. http://bioconductor.org/books/3.14/OS...
Chapters:
0:00 Intro
0:51 Overview of cell annotation workflow
2:45 Strategies for automatic cell annotation
4:06 Marker-based annotation approach
5:41 Reference-based annotation approach
7:05 How does SingleR work?
9:30 Study design and goal of the analysis
10:09 Data used for demonstration
10:27 Reading data, filtering and pre-processing in Seurat
15:41 Pointers to choose reference dataset to run SingleR
18:11 Fetching reference data from celldex package
20:20 Run SingleR()
22:04 Understanding singleR output
23:04 Visualize singleR labels in a UMAP plot
25:03 Annotation diagnostic 1: Based on scores within cells
28:02 Annotation diagnostic 2: Based on deltas across cells
29:13 Annotation diagnostic 3: Comparing cell type assignments to unsupervised clustering
You can show your support and encouragement by buying me a coffee:
https://www.buymeacoffee.com/bioinfor...
To get in touch:
Website: https://bioinformagician.org/
Github: https://github.com/kpatel427
Email: [email protected]
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