Answers.Body: <p>The Barcode Rank plot shows very high background in your sample.<br />
  The cell estimation from <code>Cell Ranger</code> 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 <a href="https://cellbender.readthedocs.io/en/latest/" rel="nofollow noreferrer">CellBender</a> your case best corresponds to the rightmost example image.</p>
  <p>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.</p>
  <p>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.</p>
  <p>Using software such as CellBender, <a href="https://github.com/constantAmateur/SoupX" rel="nofollow noreferrer">SoupX</a> or <a href="https://bioconductor.org/packages/release/bioc/html/DropletUtils.html" rel="nofollow noreferrer">DropletUtils</a> may help with identifying and reducing the background. CellBender also will rerun the cell estimation step, automatically adjusting the number of called cells.</p>
  <p>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.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-08-24T18:22:05.960Z
Answers.Text: 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?
Answers.UserDisplayName: Yulia Kentieva
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-08-25T13:37:54.607Z
Answers.Text: 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.
Answers.UserDisplayName: PPK
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Answers.CreationDate: 2022-08-23T18:15:14.077Z
Answers.LastActivityDate: 2022-08-23T18:15:14.077Z
Answers.UserDisplayName: PPK
Body: <p>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 <code>feature_bc_matrix</code> has almost 2 million cells.</p>
  <p><a href="https://i.sstatic.net/4Lcsd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/4Lcsd.png" alt="enter image description here" /></a></p>
  <p>How to tune cellranger count to get lower number of cells?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-22T19:22:12.273Z
Comments.Text: 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.
Comments.UserDisplayName: Community
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-23T06:35:18.637Z
Comments.Text: 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.
Comments.UserDisplayName: PPK
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-23T13:41:24.497Z
Comments.Text: yes, I added it. Thank you. Is that a library preparation issue then? I am planning to use cellbender to remove ambient signal.
Comments.UserDisplayName: Yulia Kentieva
Tags: <scrnaseq><single-cell><10x-genomics><cellranger>
Title: Cellranger results have too many cells
ThreadText: # Cellranger results have too many cells
  
  (Asked by: username_0 on 2022-08-22)
  
  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.
  
  [![enter image description here](https://i.sstatic.net/4Lcsd.png)](https://i.sstatic.net/4Lcsd.png)
  
  How to tune cellranger count to get lower number of cells?
  
  **Tags:** scrnaseq, single-cell, 10x-genomics, cellranger
  
  > **Comments:**
  > **username_1**: 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.  
  > **username_2**: 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.  
  > **username_0**: yes, I added it. Thank you. Is that a library preparation issue then? I am planning to use cellbender to remove ambient signal.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-08-23. Score: 2 | Accepted answer
  
  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](https://cellbender.readthedocs.io/en/latest/) 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](https://github.com/constantAmateur/SoupX) or [DropletUtils](https://bioconductor.org/packages/release/bioc/html/DropletUtils.html) 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.
  
  > **Comments:**
  > **username_0**: 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?  
  > **username_2**: 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.  
  
  ---
  
  