<p>If you've got enough replicates (ideally at least 6, but can be done with 3), you should be able to do a Variance-stabilised Transformation using DESeq2:</p>
<pre><code>vsd &lt;- vst(dds, blind=FALSE)
rld &lt;- rlog(dds, blind=FALSE)
head(assay(vsd), 3)
</code></pre>
<p>More details on this can be found in the DESeq2 Vignette:</p>
<p><a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#extracting-transformed-values" rel="nofollow noreferrer">https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#extracting-transformed-values</a></p>

CC BY-SA 4.0
2021-11-12T18:27:38.933Z
2021-11-12T18:27:38.933Z
gringer
<p><em>Answer from @james-hawley, converted from <a href="https://bioinformatics.stackexchange.com/help/privileges/comment">comment</a>:</em></p>
<p>In any high throughput sequencing experiment, there will be lowly expressed RNA molecules that are not captured by the library prep + sequencing. So defining what &quot;is expressed&quot; is somewhat arbitrary from RNA-seq data, regardless of how many replicates you have and what method you use. That said, you can control some of the noise in your RNA-seq data using a variance-stabilized transformation, like @gringer suggests, and look at the distribution of genes/transcripts. Some threshold like <span class="math-container">$rlog \ge x$</span> in at least <span class="math-container">$n$</span> samples is usually a good starting point (for some arbitrary <span class="math-container">$x$</span> and <span class="math-container">$n$</span>).</p>

CC BY-SA 4.0
2022-10-06T06:56:58.613Z
2022-10-06T06:56:58.613Z
gringer
<p>I don't think there is a very good way to do what you want to do.  I'd think some kind of RPM would be better than strictly raw counts; you have to account for overall library size.  Or TPM might be better.</p>

CC BY-SA 4.0
2022-10-08T18:58:06.617Z
2022-10-08T18:58:06.617Z
swbarnes2
<p>I want to look at an RNA-seq data-set on 1 condition to look for expressed genes. As there is only one condition, I can’t perform differential analysis.</p>
<p>The only thing I can think to do is set a threshold (i.e. all replicates must have &gt; 10 gene counts) then say these are expressed. But I don’t think this is a very good way of doing it.</p>
<p>Do you know of any better ways that we can do this?</p>

<rna-seq><differential-expression>
Gene expression changes in one condition
# Gene expression changes in one condition

(Asked by: username_0 on 2021-11-12)

I want to look at an RNA-seq data-set on 1 condition to look for expressed genes. As there is only one condition, I can’t perform differential analysis.

The only thing I can think to do is set a threshold (i.e. all replicates must have > 10 gene counts) then say these are expressed. But I don’t think this is a very good way of doing it.

Do you know of any better ways that we can do this?

**Tags:** rna-seq, differential-expression

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## 3 answers

### Answer by username_1 on 2021-11-12. Score: 2

If you've got enough replicates (ideally at least 6, but can be done with 3), you should be able to do a Variance-stabilised Transformation using DESeq2:
vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
head(assay(vsd), 3)

More details on this can be found in the DESeq2 Vignette:
https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#extracting-transformed-values


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### Answer by username_1 on 2022-10-06. Score: 1

*Answer from @james-hawley, converted from [comment](https://bioinformatics.stackexchange.com/help/privileges/comment):*

In any high throughput sequencing experiment, there will be lowly expressed RNA molecules that are not captured by the library prep + sequencing. So defining what "is expressed" is somewhat arbitrary from RNA-seq data, regardless of how many replicates you have and what method you use. That said, you can control some of the noise in your RNA-seq data using a variance-stabilized transformation, like @gringer suggests, and look at the distribution of genes/transcripts. Some threshold like $rlog \ge x$ in at least $n$ samples is usually a good starting point (for some arbitrary $x$ and $n$).


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### Answer by username_2 on 2022-10-08. Score: 1

I don't think there is a very good way to do what you want to do. I'd think some kind of RPM would be better than strictly raw counts; you have to account for overall library size. Or TPM might be better.


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