Single cell seq data analysis is a wealth of options. I swear, there are 8? commercial packages out there that will help you actually make sense of thousands of noisy low-accuracy measurements of transcripts in single cells. While most people who are good at the stuff are using R or Python pipelines, it's clear they've got the lead.
We've got some good bioinformaticians in proteomics as well, though! And here is more proof.
I haven't tried it yet, but I'm dying to. I've got some single cell data from our lab sitting here from drug treated cells that were prepared over the course of a week in 2 separate batches. I think we started with 3,000 single cells at first.
On the surface, just using a boring old PCA analysis of cells from 2 batches (maybe 560 total shown here) - same cells, same standardized lab SOP for prep, same LCMS method --
Batch 1 and batch 2 are pretty darned clear.
Is scpLainer the answer? We'll see!
The example you show looks like a good fit. Feel free to get in touch.
ReplyDeleteThe code isn't merged get, but publicly available in the scplainer PR on GitHub (scp repo).
I think scplainer would be a good fit. It is available as a PR to scp. Feel free to get in touch if you have any questions, happy to help!
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