I have the results of cellranger analysis for single-nucleus RNA seq data that was done by someone else. So I do not know which parameters were used for cellranger. In the results, there are 77000 estimated cells (which is way too high) and around 800 median reads per cell. Those are filtered counts. Raw feature_bc_matrix has almost 2 million cells.

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How to tune cellranger count to get lower number of cells?

  • $\begingroup$ Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. $\endgroup$
    – Community Bot
    Aug 22, 2022 at 19:22
  • $\begingroup$ Do you have the web_summary.html file? Then you could add the barcode rank plot (top right) to your question. To me it looks as if you have a lot of background and the cell count estimation did not work well. $\endgroup$
    – PPK
    Aug 23, 2022 at 6:35
  • $\begingroup$ yes, I added it. Thank you. Is that a library preparation issue then? I am planning to use cellbender to remove ambient signal. $\endgroup$ Aug 23, 2022 at 13:41

1 Answer 1


The Barcode Rank plot shows very high background in your sample.
The cell estimation from Cell Ranger will look for a sudden drop in the number of UMIs and use this to call cells. In your case, the drop is too much to the right because the empty droplet plateau is almost completely merged with the real cells. If you have already looked at CellBender your case best corresponds to the rightmost example image.

With high background of free mRNA, even empty droplets will contain a significant number of mRNA molecules and become hard to distinguish from real cells.

This likely is a consequence of the single nulcei preparation. To isolate the nuclei, the cells necessarily need to be broken and all the cytoplasmic mRNA is freed contributing to the background. You could try removing this background in future experiments by increasing the number of washing steps after cell lysis.

Using software such as CellBender, SoupX or DropletUtils may help with identifying and reducing the background. CellBender also will rerun the cell estimation step, automatically adjusting the number of called cells.

In any case you can manually reduce the number of cells by filtering based on the minimum number of UMIs or genes. However, the higher the background the more arbitrary such a thresholding strategy will become.

  • $\begingroup$ thank you. CellBender did not really help in my case, it returned very few cells with high % of mitochondrial genes. As for manual reduction, I tried doing it on CreateSeuratObject step using Seurat R package. However, the vast majority of filtered cells have less than 2000 features. Is low UMI also consequence of nuclei preparation? $\endgroup$ Aug 24, 2022 at 18:22
  • $\begingroup$ For single nuclei you can increase the number of mapped reads by including intronic reads in the cell ranger call (--include-introns, this is the standard configuration in the latest version of cell ranger). However, in case of high background much of your sequencing depth goes towards empty droplets and your cells wind up less well covered. I think you will have make the most out of the low quality you have. Best of luck. $\endgroup$
    – PPK
    Aug 25, 2022 at 13:37

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