Hello everyone,
This is the first time for me to analyze scRNA data, compared to the example in the workflow (Seurat), I notice that I have a large amount of mtDNA, I was thinking to incread the percentage of mtDNA so that I don't loose informations but on the other end I don't want to have bad results, do you guys have any suggestion about it ?
code:
seurat_object <- subset(seurat_object, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
my cells are leukaemia cells and here you can see the image
Thank you
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written
by
Morris_Chair
1 answer
scater has some nice QC functions that will determine which cells are outliers for QC each metric and remove them. It works on SingleCellExperiment objects, which are simple to convert from/to Seurat objects. This feels a bit better than just arbitrarily setting the thresholds yourself, though the results will be pretty similar.
But as previously mentioned, a threshold around 10% for mitochondrial reads is pretty standard - just make sure most of your obviously dead cells are removed.
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I generally use the "eye-ball" test for these types of situations. Obviously the cells with >50% mito reads are bad cells.
For the rest you would probably be ok setting the mt-DNA threshold to 10-15%.
Also please use the search function: Mitochondrial Gene percentage threshold in single cell RNA-Seq