<p>Yes! Multiome seems to have cells with much higher mitochondrial gene content. I don't understand the biology behind this. But in using a cut off of 5% (i.e. remove cells with &gt;5% MT genes) I lose over 80% of cells in multiome data. Also, here is the Signac tutorial where they use a 20% cut off for multiome data - <a href="https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html" rel="nofollow noreferrer">https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html</a> (&quot;WNN analysis of 10x Multiome, RNA + ATAC&quot;)</p>

CC BY-SA 4.0
2023-04-18T15:52:55.760Z
2023-04-18T15:52:55.760Z
Prasida Holla
<p>As I mentioned in <a href="https://bioinformatics.stackexchange.com/a/17513/73">my</a> <a href="https://bioinformatics.stackexchange.com/a/16448/73">answers</a> to similar questions, there are a lot of cells with very high mitochondrial gene content. It would be a good idea to work out why that is before doing any more downstream processing.</p>
<p>These cells will, of course, be filtered out by the thresholds you have set.</p>

CC BY-SA 4.0
2021-08-11T01:24:48.413Z
2021-08-11T01:24:48.413Z
gringer
<p>I have <code>10X scRNA-seq multiome's 3' poly-A capture (scRNA-seq+ATAC-seq)</code> from PBMC</p>
<p>Using Seuratreference mapping, I mapped my scRNA-seq part on reference PBMC (for cell type annotation)</p>
<p>I have also tried Seurat zimuth shiny app</p>
<p>But likely just a few of my cells has been mapped on the reference</p>
<p>Please look at this</p>
<p><a href="https://i.sstatic.net/UEGmA.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/UEGmA.jpg" alt="enter image description here" /></a></p>
<p>Do you think this because my data is multiome or something else is going wrong?</p>
<p>Please any body has some experience help me</p>
<p>Thank you</p>

<scrnaseq><seurat><sequence-annotation><10x-genomics><cellranger>
Problem with Seurat reference mapping
# Problem with Seurat reference mapping

(Asked by: username_0 on 2021-08-10)

I have `10X scRNA-seq multiome's 3' poly-A capture (scRNA-seq+ATAC-seq)` from PBMC

Using Seuratreference mapping, I mapped my scRNA-seq part on reference PBMC (for cell type annotation)

I have also tried Seurat zimuth shiny app

But likely just a few of my cells has been mapped on the reference

Please look at this

[![enter image description here](https://i.sstatic.net/UEGmA.jpg)](https://i.sstatic.net/UEGmA.jpg)

Do you think this because my data is multiome or something else is going wrong?

Please any body has some experience help me

Thank you

**Tags:** scrnaseq, seurat, sequence-annotation, 10x-genomics, cellranger

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## 2 answers

### Answer by username_1 on 2023-04-18. Score: 1

Yes! Multiome seems to have cells with much higher mitochondrial gene content. I don't understand the biology behind this. But in using a cut off of 5% (i.e. remove cells with >5% MT genes) I lose over 80% of cells in multiome data. Also, here is the Signac tutorial where they use a 20% cut off for multiome data - <https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html> ("WNN analysis of 10x Multiome, RNA + ATAC")


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### Answer by username_2 on 2021-08-11. Score: 0

As I mentioned in [my](https://bioinformatics.stackexchange.com/a/17513/73) [answers](https://bioinformatics.stackexchange.com/a/16448/73) to similar questions, there are a lot of cells with very high mitochondrial gene content. It would be a good idea to work out why that is before doing any more downstream processing.

These cells will, of course, be filtered out by the thresholds you have set.


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