How to deal with single nucleus data in Seurat
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9 months ago

Dear all, I am by no means a bioinformatician, but because of the pandemic I have been looking for parts of my work I can do from home, so please forgive me if I ask extremely basic questions.

I obtained a data set of single nuclei from the Drosophila antenna, and my boss suggested using the Seurat pipeline to analyse the data. I did the Seurat tutorials but none of them are specific for nuclei.

Does anyone have experience how to deal with this kind of data or can point me into the direction of some more information? For example in the QC step mitochondrial genes are excluded but my nuclei data should not have any of those to begin with.

Thank you for your advice, any help is appreciated.
Kristina

RNA-Seq seurat single nucleus RNA sequencing • 701 views
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You can proceed as normal with single nuclei data.

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5 months ago
Pratik Mehta ▴ 420

Hi Kristina,

Rpolicastro is right.

There should be no difference in either using single cell RNA seq data or single nuclear RNA seq data with Seurat.

A good place to start is definitely: https://satijalab.org/seurat/articles/pbmc3k_tutorial.html

Many of the steps are modifiable, based on your judgement. If you know that your data doesn't have any mitochondrial gene contamination, than you could definitely just skip that step.

pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

or the following which I use cause because my expression matrix lists the mitochondrial genes as MT... rather than MT-...

pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT")
VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

just shows the mitochondrial genes.

The subset step where you do:

pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

You can certainly just remove the & percent.mt < 5 (MT gene filtering) to just pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500). Furthermore, as you may know the filtering numbers are also up to you to decide where you want to draw the line in QC, based on your analysis of the previous plots generated.

Hope this helps!

Feel free to ask any other questions. I think I've got a decent grasp on Seurat. : )

Sincerely, Pratik

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