Hello,
I am new to single cell RNA data processing and am currently running my data through the initial cleanup steps via Seurat on R. I have previously done this on epithelial organoid data and am wandering why there is such high percentage of cells with mitochondrial RNA counts as well as such a high number of cells with a high number of unique counts.
The samples are from the spleens obtained from patients/donors that have or had PDAC.
When I apply the same settings in R:
Attached are the plots obtained prior to filtering.
(s1_1 3000 & nFeature_RNA < 9500 & percent.mt < 25)
I end up with a very small number of cells that have acceptable features for further analysis.
I typically do not work with immune cell populations so I am sure there are some things I am unaware of or am overlooking. Also doing this just to begin to learn how to process scRNA data.
Any direction to good learning resources would be much appreciated.
Thanks for your time, any input is welcome!
Thank you
Kyle
Hi Kyle,
Looking at your code and the figures you generated. It seems setting minimum number of features to 3000 is too stringent and you may want to reduce this ~1000.
I've seen some experiment with more MT%, You may want to try with 20% as a cutoff for MT.
Also, you may want to remove doublet cells as well.
Best,
Amir
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