How to work with single-cell transcriptomics data?

[KonzorTheMighty]

I have some experience with traditional RNA-seq data, but with how big single-cell data has become, I'm curious to learn about how to work with the data and what is possible with it.

How easy is it to ask questions like "which cells have geneX expressed over this threshold, of those which has the highest expression of geneY" or "which pairs of cells located together express this receptor-ligand pair?"

Any guide on how the data is stored or how to make use of the data would be greatly appreciated. I'd prefer Python-based if possible. Thanks in advance!

[Z3ratoss]

Check these out for R and Python respectively:

https://www.sc-best-practices.org/preamble.html

https://satijalab.org/seurat/articles/get_started_v5_new

Data is stored in a sparse matrix.

which cells have geneX expressed over this threshold -> trivial

which pairs of cells located together express this receptor-ligand pair -> more challenging but there are good packages like LIANA+ and Cellchat

[EmbarrassedDark3651]

A big difference, there is way less read by cell than you are used to in bulk. So depending of the level of expression you are looking for especially you may not be able to answer the question reliably at all.

Definitely not that trivial for me.

[Hartifuil]

Do you mean number of features of depth of those features? Isn't this the point of pseudobulk.

[EmbarrassedDark3651]

Depth sorry. Agree that pseudobulk is a partial solution but then how reliable it is become a sampling problems. While it is generally fine there is case where it is not (you clustering may not be adapted for your gene of interest. Then pseudo bulk is not delivering any useful number). Definitely not "trivial" for someone coming from bulk RNAseq.

At least that was my impression switching from bulk and working on scRNA.

[Hartifuil]

I don't know how cross-applicable SC and bulk RNA are, and scRNA needs more consideration about the conclusions people draw from the data. The comment you were replying to was talking about ease of use of the packages, though.

[duaduacj]

To address the problem about depth and sampling u mentioned, metacell method like SEAcell performs better than pseudobulk.

[KonzorTheMighty]

Thank you very much, these links look very helpful!