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Difference between number of cells predicted by two different versions of cellranger

Hii,

I am trying CellRanger for my single-cell transcriptomic data (3’ Gene Expression v2 Libraries). I run the analysis on this data using two different versions of CellRanger v2.2.0 and v6.1.0, the number of cells in both analyses are drastically different. with v2.2.0 we are getting an average of ~8000 cells whereas with the new version we are getting ~11000.

What could be the possible reason behind this and which version should I use?

Any suggestion will be greatly appreciated.

Regards, Nitin N.

cellranger scrna-seq
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updated by jared.andrews07 jared.andrews07 • written by Nitin Narwade Nitin Narwade

1 answer

This is (likely) primarily due to the swap to using a version of the EmptyDrops algorithm for cell calling in version 3.0.

Cell Calling Changes Cell Ranger 3.0 implements a version of the EmptyDrops cell calling algorithm that will call more low RNA content cells, especially when they are mixed with a population of high RNA content cells. See Cell Calling Algorithms for details. The cell calling 'knee-plot' in the web summary now indicates what fraction of barcodes in each segment of the curve were called as cells, since the new cell calling algorithm no longer makes a hard threshold on UMI counts.

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written by jared.andrews07 jared.andrews07

Thanks, Jared,

I don't know how I missed this while reading. Probably I checked the major changes only for CellRanger v2.2.0 and v6.1.0.

Thank you for the information. So do you have any suggestions for removing the low RNA content cells from the downstream?

BTW, I am using Seurat for the downstream analysis. Currently, I am using this code for the initial QC (following this tutorial).

filtered_seurat <- subset(x = merged_seurat, 
                     subset= (nUMI >= 500) &  # number of UMI per cell
                       (nGene >= 500) &   # minimum number of genes per cell
                       (nGene <= 4000) & # maximum number of genes per cell
                       (log10GenesPerUMI > 0.85) & # complexity ratio: 
                       (mitoRatio < 0.05) & 
                       (doublet_status == FALSE))

Do you have any suggestions on this to remove cells with low RNA content?

Thanks and Regards,

Nitin N.

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written by Nitin Narwade Nitin Narwade

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