Remove contamination across cell clusters
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2.5 years ago

I've got an scRNA-seq dataset filtered for immune cells. After applying the Seurat workflow I've discovered that some non-immune markers are expressed by cell subsets in almost all the clusters. See heat map:

enter image description here

I'm fairly certain that those cells are contamination. How would you filter those out based on expression values?

Thanks in advance!

clustering single-cell • 3.0k views
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Why do you think those are contaminating cells?

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How can you say that these are contaminated? For which cell types the selections are done in scRNA-seq and which non-immune cell types you are getting?

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rpolicastro @EagleEye that's a good question! The cells were sorted by CD45+ before sequencing. Some epithelial cells were detected nonetheless, based on EPCAM+. Now, these cells expressing salivary markers - I'm not 100% sure they're contaminating. It might as well be that the immune cells in the tissue are expressing some tissue-specific genes. But my original question is more about the technical process of removing these cells if they indeed are contamination.

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2.5 years ago
firestar ★ 1.6k

There are many ways to do this. Here is a working example.

library(Seurat)
pbmc_small <- pbmc_small %>%
              NormalizeData() %>%
              FindVariableFeatures() %>%
              ScaleData()

VlnPlot(pbmc_small,features="PPBP")

enter image description here

Let's say we want to remove cells expressing PPBP above 5.5. Here is one way:

# get positions of genes that match condition
x <- which(GetAssayData(pbmc_small)["PPBP",]<5.5)
# subset to new object
pbmc_small1 <- subset(pbmc_small,cells=x)

VlnPlot(pbmc_small1,features="PPBP")

enter image description here

Here is another way. This way, you can plot your good and bad cells on a UMAP for example before deciding to remove it.

# save good and bad cells as a new metadata variable
pbmc_small$good <- GetAssayData(pbmc_small)["PPBP",]<5.5
# UMAPPlot(pbmc_small,group.by="good")
pbmc_small1 <- subset(pbmc_small,subset=good==TRUE)
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Thanks, this makes sense! I was also wondering if there's some semi-automated threshold detection method that is used in practice.

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