<p>I found a recent technique that allows for both, called <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2246-7" rel="nofollow noreferrer">geTMM</a>.</p>

CC BY-SA 4.0
2021-07-09T13:03:48.607Z
2021-07-09T13:03:48.607Z
An Ignorant Wanderer
<p>I'm having a bit of trouble understanding the question, but we've used DESeq2 for doing both between-sample and between-transcript comparisons.</p>
<p>When comparing transcripts for short reads I use the variant-stabilised transformation, adjusted for transcript length. This is good for a visual overview of results (e.g. in a heatmap).</p>
<p>When comparing samples I use the variance-adjusted DESeq2 test, producing normalised <span class="math-container">$\textrm{log}_2FC$</span> values. This works well for quantitative results.</p>
<p>See <a href="https://doi.org/10.1084/jem.20160470" rel="nofollow noreferrer">this paper</a>, where we use both of these approaches (e.g. Figure 2 shows both in a side-by-side view).</p>

CC BY-SA 4.0
2021-07-08T03:32:18.667Z
2021-07-08T03:32:18.667Z
gringer
<p>As mentioned by @gringer DESeq2 is a popular choice for normalization of counts between samples. However, because each gene is adjusted individually it is not well suited to compare within a sample.<br />
For this a global normalization like <code>TMM</code> from <code>edgeR</code> would be more appropriate.</p>
<p>You can find more information in this <a href="https://hbctraining.github.io/DGE_workshop_salmon_online/lessons/02_DGE_count_normalization.html" rel="nofollow noreferrer">tutorial</a> from the hbc at Harvard.</p>

CC BY-SA 4.0
2021-07-09T10:01:47.310Z
2021-07-09T10:01:47.310Z
PPK
<p>What is a good RNA seq normalization method that allows for across sample comparisons, <strong>and</strong> allows between transcripts comparisons as well? I read that TMM for example allows across sample comparisons but not between transcripts. On the other hand, I read that TPM is not good enough for between sample comparisons </p>

<rna-seq><normalization>
What is a good RNA seq normalization method that allows for across sample comparisons and between transcripts
# What is a good RNA seq normalization method that allows for across sample comparisons and between transcripts

(Asked by: username_0 on 2020-06-12)

What is a good RNA seq normalization method that allows for across sample comparisons, **and** allows between transcripts comparisons as well? I read that TMM for example allows across sample comparisons but not between transcripts. On the other hand, I read that TPM is not good enough for between sample comparisons

**Tags:** rna-seq, normalization

---

## 3 answers

### Answer by username_0 on 2021-07-09. Score: 1 | Accepted answer

I found a recent technique that allows for both, called [geTMM](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2246-7).


---

### Answer by username_1 on 2021-07-08. Score: 0

I'm having a bit of trouble understanding the question, but we've used DESeq2 for doing both between-sample and between-transcript comparisons.

When comparing transcripts for short reads I use the variant-stabilised transformation, adjusted for transcript length. This is good for a visual overview of results (e.g. in a heatmap).

When comparing samples I use the variance-adjusted DESeq2 test, producing normalised $\textrm{log}\_2FC$ values. This works well for quantitative results.

See [this paper](https://doi.org/10.1084/jem.20160470), where we use both of these approaches (e.g. Figure 2 shows both in a side-by-side view).


---

### Answer by username_2 on 2021-07-09. Score: 0

As mentioned by @gringer DESeq2 is a popular choice for normalization of counts between samples. However, because each gene is adjusted individually it is not well suited to compare within a sample.  
For this a global normalization like `TMM` from `edgeR` would be more appropriate.

You can find more information in this [tutorial](https://hbctraining.github.io/DGE_workshop_salmon_online/lessons/02_DGE_count_normalization.html) from the hbc at Harvard.


---

