<p>The rRNA genes in that dataset are <code>Rn45s</code> and <code>Rn4.5s</code>.</p>

<p>BTW, you have gene counts, not transcript counts.</p>

CC BY-SA 3.0
2018-01-03T15:25:22.130Z
Thanks. What do you mean by gene counts instead of transcript counts? That the counts are already collapsed by gene?
gc5
CC BY-SA 3.0
2018-01-03T15:32:23.180Z
Also, how did you find the list of rRNA genes in such dataset?
gc5
CC BY-SA 3.0
2018-01-03T15:34:45.983Z
@gc5 The counts were likely only ever done by genes to begin with. rRNA genes typically have names starting with "Rn" in mouse.
Devon Ryan
CC BY-SA 3.0
2018-01-03T15:42:15.960Z
Ok. Should I need to find ribosomal genes in human do you know any database which I can use?
gc5
CC BY-SA 3.0
2018-01-03T15:43:08.790Z
I think they have similar names in humans.
Devon Ryan
CC BY-SA 3.0
2018-01-02T22:42:57.440Z
2018-01-02T22:42:57.440Z
Devon Ryan
<p>In the paper mentioned, we used the <code>ScaleData</code> function in Seurat to regress out the number of reads, Rn45s abundance, and percent ribosomal gene transcripts. Ribosomal genes were found with the regular expression <code>^Rp[sl][[:digit:]]</code>.</p>

<pre><code>tiss &lt;- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
</code></pre>

<p><a href="https://github.com/czbiohub/maca/blob/master/MACA_Plate_Notebook.Rmd" rel="nofollow noreferrer">Here's</a> a fuller notebook, and we'll have a better organized repository soon.</p>

CC BY-SA 4.0
2018-12-18T01:46:10.473Z
That's great that the workflow is available. I am just curious why did you decide to use ribosomal genes instead of the mitochondrial genes, which is suggested by Seurat?
burger
CC BY-SA 4.0
2018-12-18T21:51:51.867Z
Hello! Thanks for asking. In the final version of the paper (this question cites the preprint), we ultimately decided *not* to normalize on either ribosomal or mitochondrial genes. We found that this normalization added differences where there shouldn't be any, e.g. between cells of the same type in the Bladder.
Olga Botvinnik
CC BY-SA 4.0
2018-12-18T22:41:05.943Z
Thanks for clarifying! I was wondering why I could not find any mention of ribosomal genes anywhere in the methods (in the final paper).
burger
CC BY-SA 3.0
2018-01-05T05:49:59.280Z
2018-01-05T09:27:50.420Z
2018-01-05T09:27:50.420Z
Olga Botvinnik
<p>I want to filter out ribosomal RNA from scRNA-seq data (downloaded from <a href="https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733" rel="nofollow noreferrer">here</a>). Is there a list of known ribosomal RNA?</p>

<p>The only solution I found is <a href="http://bioinfo.lifl.fr/RNA/sortmerna/" rel="nofollow noreferrer">SortMeRNA</a>, however it works with raw sequencing data afaik, while I already have a matrix with transcript counts for each cell. I searched for a comprehensive list of rRNAs but I didn't find any.</p>

CC BY-SA 3.0
2018-01-02T22:23:01.093Z
What GTF file did you use to generate the counts? For some species only 5S (and maybe MT) rRNA is left, for others you might have one or more copies of the various 48S components.
Devon Ryan
CC BY-SA 3.0
2018-01-02T22:27:06.967Z
I actually don't have GTF files. I downloaded the data provided by the Tabula Muris Consortium (https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733)
gc5
<rna-seq><scrnaseq><rna><ribosomal>
How to filter ribosomal RNA from scRNA-seq data
# How to filter ribosomal RNA from scRNA-seq data

(Asked by: username_0 on 2018-01-02)

I want to filter out ribosomal RNA from scRNA-seq data (downloaded from [here](https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733)). Is there a list of known ribosomal RNA?

The only solution I found is [SortMeRNA](http://bioinfo.lifl.fr/RNA/sortmerna/), however it works with raw sequencing data afaik, while I already have a matrix with transcript counts for each cell. I searched for a comprehensive list of rRNAs but I didn't find any.

**Tags:** rna-seq, scrnaseq, rna, ribosomal
> **Comments:**
> **username_1**: What GTF file did you use to generate the counts? For some species only 5S (and maybe MT) rRNA is left, for others you might have one or more copies of the various 48S components.  
> **username_0**: I actually don't have GTF files. I downloaded the data provided by the Tabula Muris Consortium (https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733)  

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## 2 answers

### Answer by username_1 on 2018-01-02. Score: 3 | Accepted answer

The rRNA genes in that dataset are `Rn45s` and `Rn4.5s`.

BTW, you have gene counts, not transcript counts.

> **Comments:**
> **username_0**: Thanks. What do you mean by gene counts instead of transcript counts? That the counts are already collapsed by gene?  
> **username_0**: Also, how did you find the list of rRNA genes in such dataset?  
> **username_1**: @gc5 The counts were likely only ever done by genes to begin with. rRNA genes typically have names starting with "Rn" in mouse.  
> **username_0**: Ok. Should I need to find ribosomal genes in human do you know any database which I can use?  
> **username_1**: I think they have similar names in humans.  

---

### Answer by username_2 on 2018-01-05. Score: 6

In the paper mentioned, we used the ScaleData function in Seurat to regress out the number of reads, Rn45s abundance, and percent ribosomal gene transcripts. Ribosomal genes were found with the regular expression ^Rp[sl][[:digit:]].
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))

Here's a fuller notebook, and we'll have a better organized repository soon.

> **Comments:**
> **username_3**: That's great that the workflow is available. I am just curious why did you decide to use ribosomal genes instead of the mitochondrial genes, which is suggested by Seurat?  
> **username_2**: Hello! Thanks for asking. In the final version of the paper (this question cites the preprint), we ultimately decided *not* to normalize on either ribosomal or mitochondrial genes. We found that this normalization added differences where there shouldn't be any, e.g. between cells of the same type in the Bladder.  
> **username_3**: Thanks for clarifying! I was wondering why I could not find any mention of ribosomal genes anywhere in the methods (in the final paper).  

---

