<p>In an ideal world, ribosomal RNA (as seen in your top hit) should be excluded from samples <em>prior</em> to sequencing. Where this is not possible (i.e. in the data that have been presented to you), it would be a good idea to exclude ribosomal genes (and any other common contaminants) prior to doing further analysis (including normalisation). I believe this is the point the course instructors are trying to get across with that message box.</p>

<p>Eyeballing the results, you should also exclude any ERCC genes as well, as they are synthetic constructs and not from the target samples (although record their counts, as it may be informative for QC/normalisation purposes); they'll have odd distributions because of this.</p>

<p>ERCC sequences are typically added based on the concentration of DNA in a sample, rather than the number of cells. However with single-cell data (i.e. with known cell counts), it'd be possible to do a proper ERCC-based normalisation, assuming an equal amount were added for each cell. I suspect that knowing a bit more about the ERCC addition protocol would help in understanding why their distribution is so different.</p>

CC BY-SA 3.0
2018-01-02T21:21:52.203Z
Thanks! I noticed that the original comment for the analysis was not included in the question: I updated the question, I didn't get what they meant with flat distribution.
gc5
CC BY-SA 3.0
2018-01-15T20:12:04.987Z
I forgot to ask, why remove ribosomal genes? I tried to find an explanation of this contamination, but with no results.
gc5
CC BY-SA 3.0
2018-01-16T00:52:29.763Z
As you've noticed from your own analysis, the ribosomal genes have quite variable expression across cells. They're expressed everywhere, and quite difficult to completely remove from a sample (even with polyA selection), with success of removal depending on things like the amount of mechanical damage that the RNA has been exposed to. This high, variable expression throws out normalisation calculations, so it's better to remove them first.
gringer
CC BY-SA 3.0
2018-01-02T20:57:57.067Z
2018-01-02T23:19:43.323Z
2018-01-02T23:19:43.323Z
gringer
<p>I am running the data preprocessing pipeline for scRNA-seq data presented <a href="https://hemberg-lab.github.io/scRNA.seq.course/construction-of-expression-matrix.html#gene-analysis" rel="nofollow noreferrer">here</a>.</p>
<blockquote>
<p><strong>3.8.6.1 Gene expression</strong></p>
<p>In addition to removing cells with poor quality, it is usually a good idea to exclude genes where we suspect that technical artefacts may have skewed the results. Moreover, inspection of the gene expression profiles may provide insights about how the experimental procedures could be improved.</p>
<p>It is often instructive to consider the number of reads consumed by the top 50 expressed genes.</p>
</blockquote>
<p>By analyzing my data I have the following most expressed 50 genes (evaluating the total number of transcripts across cells):</p>
<p><strong>Original</strong></p>
<p><a href="https://i.sstatic.net/CCLEl.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/CCLEl.png" alt="linear" /></a></p>
<p><strong>Log-transformed</strong></p>
<p><a href="https://i.sstatic.net/91QoD.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/91QoD.png" alt="log" /></a></p>
<p>Since I'll be working on log-transformed data, I assumed that the distributions of the top 50 genes were not excessively skewed. However, as you can see in the first plot, there are some samples (cells) with very high number of transcripts for the top gene. Is it something I should worry about? What other insights can I get from these two figures?</p>
<h2>Edit</h2>
<p>Original analysis (figure attached) was:</p>
<p><a href="https://i.sstatic.net/k0db3.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/k0db3.png" alt="enter image description here" /></a></p>
<blockquote>
<p>The distributions are relatively flat indicating (but not guaranteeing!) good coverage of the full transcriptome of these cells. However, there are several spike-ins in the top 15 genes which suggests a greater dilution of the spike-ins may be preferrable if the experiment is to be repeated.</p>
</blockquote>

<gene><scrnaseq><homework><data-preprocessing>
Inspection of gene expression in scRNA-seq data
# Inspection of gene expression in scRNA-seq data

(Asked by: username_0 on 2017-12-28)

I am running the data preprocessing pipeline for scRNA-seq data presented [here](https://hemberg-lab.github.io/scRNA.seq.course/construction-of-expression-matrix.html#gene-analysis).

> **3.8.6.1 Gene expression**
>
> In addition to removing cells with poor quality, it is usually a good idea to exclude genes where we suspect that technical artefacts may have skewed the results. Moreover, inspection of the gene expression profiles may provide insights about how the experimental procedures could be improved.
>
> It is often instructive to consider the number of reads consumed by the top 50 expressed genes.

By analyzing my data I have the following most expressed 50 genes (evaluating the total number of transcripts across cells):

**Original**

[![linear](https://i.sstatic.net/CCLEl.png)](https://i.sstatic.net/CCLEl.png)

**Log-transformed**

[![log](https://i.sstatic.net/91QoD.png)](https://i.sstatic.net/91QoD.png)

Since I'll be working on log-transformed data, I assumed that the distributions of the top 50 genes were not excessively skewed. However, as you can see in the first plot, there are some samples (cells) with very high number of transcripts for the top gene. Is it something I should worry about? What other insights can I get from these two figures?

## Edit

Original analysis (figure attached) was:

[![enter image description here](https://i.sstatic.net/k0db3.png)](https://i.sstatic.net/k0db3.png)

> The distributions are relatively flat indicating (but not guaranteeing!) good coverage of the full transcriptome of these cells. However, there are several spike-ins in the top 15 genes which suggests a greater dilution of the spike-ins may be preferrable if the experiment is to be repeated.

**Tags:** gene, scrnaseq, homework, data-preprocessing

---

## 1 answers

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

In an ideal world, ribosomal RNA (as seen in your top hit) should be excluded from samples *prior* to sequencing. Where this is not possible (i.e. in the data that have been presented to you), it would be a good idea to exclude ribosomal genes (and any other common contaminants) prior to doing further analysis (including normalisation). I believe this is the point the course instructors are trying to get across with that message box.

Eyeballing the results, you should also exclude any ERCC genes as well, as they are synthetic constructs and not from the target samples (although record their counts, as it may be informative for QC/normalisation purposes); they'll have odd distributions because of this.

ERCC sequences are typically added based on the concentration of DNA in a sample, rather than the number of cells. However with single-cell data (i.e. with known cell counts), it'd be possible to do a proper ERCC-based normalisation, assuming an equal amount were added for each cell. I suspect that knowing a bit more about the ERCC addition protocol would help in understanding why their distribution is so different.

> **Comments:**
> **username_0**: Thanks! I noticed that the original comment for the analysis was not included in the question: I updated the question, I didn't get what they meant with flat distribution.  
> **username_0**: I forgot to ask, why remove ribosomal genes? I tried to find an explanation of this contamination, but with no results.  
> **username_1**: As you've noticed from your own analysis, the ribosomal genes have quite variable expression across cells. They're expressed everywhere, and quite difficult to completely remove from a sample (even with polyA selection), with success of removal depending on things like the amount of mechanical damage that the RNA has been exposed to. This high, variable expression throws out normalisation calculations, so it's better to remove them first.  

---

