Hi, I am working on a single-cell dataset, would you have any recommendations for tools/algos for the following two aspects
After dimensionality reduction and cell clustering, we have annotated clusters based on general understanding of immunology (e.g. B cells annotated as such because they express MS4A1, CD79a strongly etc). But is there a way to compare quantitatively how well the transcriptional signatures of populations in the published literature (say of B cells, taken from bulk RNA seq) matches our cluster annotation as B cells? What would be the best algorithm for this "scoring"?
We have carried out bulk RNA sequencing of certain sub-populations purified by flow cytometry. We have a list of top DEGs expressed by these purified populations. Is there a way to highlight the cells which express these DEGs on the single-cell dataset? (Using Seurat, we can light up cells expressing one gene. But how would we do the same for a list of genes?)
Many thanks for any suggestions!
Hello, I have scRNA seq data with Gene Ensemble ids, I don't know how to force SingleR to use gene ids instead of gene names to annotate the cell types. any recommendations would be appreciated.
If you're using any of the built-in reference datasets, you can feed
ensembl=TRUEto the function when you retrieve them to use ensemble IDs rather than gene symbols. You'll likely lose a handful of genes as the mapping is never perfect, but it generally works well.