1
$\begingroup$

I want to filter out ribosomal RNA from scRNA-seq data (downloaded from here). Is there a list of known ribosomal RNA?

The only solution I found is 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.

Improve this question
$\endgroup$
2
  • $\begingroup$ 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. $\endgroup$
    – Devon Ryan
    Commented Jan 2, 2018 at 22:23
  • $\begingroup$ I actually don't have GTF files. I downloaded the data provided by the Tabula Muris Consortium (figshare.com/projects/…) $\endgroup$
    – gc5
    Commented Jan 2, 2018 at 22:27

2 Answers 2

Reset to default
3
$\begingroup$

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

BTW, you have gene counts, not transcript counts.

Improve this answer
$\endgroup$
5
  • $\begingroup$ Thanks. What do you mean by gene counts instead of transcript counts? That the counts are already collapsed by gene? $\endgroup$
    – gc5
    Commented Jan 3, 2018 at 15:25
  • $\begingroup$ Also, how did you find the list of rRNA genes in such dataset? $\endgroup$
    – gc5
    Commented Jan 3, 2018 at 15:32
  • 1
    $\begingroup$ @gc5 The counts were likely only ever done by genes to begin with. rRNA genes typically have names starting with "Rn" in mouse. $\endgroup$
    – Devon Ryan
    Commented Jan 3, 2018 at 15:34
  • $\begingroup$ Ok. Should I need to find ribosomal genes in human do you know any database which I can use? $\endgroup$
    – gc5
    Commented Jan 3, 2018 at 15:42
  • 1
    $\begingroup$ I think they have similar names in humans. $\endgroup$
    – Devon Ryan
    Commented Jan 3, 2018 at 15:43
6
$\begingroup$

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.

Improve this answer
$\endgroup$
3
  • $\begingroup$ 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? $\endgroup$
    – burger
    Commented Dec 18, 2018 at 1:46
  • 1
    $\begingroup$ 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. $\endgroup$
    – Olga Botvinnik
    Commented Dec 18, 2018 at 21:51
  • $\begingroup$ Thanks for clarifying! I was wondering why I could not find any mention of ribosomal genes anywhere in the methods (in the final paper). $\endgroup$
    – burger
    Commented Dec 18, 2018 at 22:41

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.