<p>TLDR- just do the analysis in DESeq2 (it can handle multiple covariates)</p>
<p>If I've interpreted your question correctly there seem to be a few confusions here.</p>
<p>First off, for a multiple linear regression aka ANCOVA, one of the assumptions are the <em>residuals</em> are normally distributed - however this is almost never an issue in practice. The distribution of the data is not so important, you make the assumption that the data has come from a population which is continuous and normally distributed that's why you've chosen to do an ANCOVA. But, RNAseq produces count data - not continuous and bounded at zero. You need to be looking at using a Poisson GLM, or negative binomial GLM as done by DESeq2 (lookup overdispersion)</p>
<p>The normalisation done by DESeq is to account for different library sizes,it doesn't <em>transform</em> the data to make it conform to a normal distribution. You'd need to do some form of data transformation to do that, but don't it's unnecessary.</p>
<p>I'd suggest reading the DESeq vignettes and following their standard pipeline.</p>
<p>Hope that is of some help.</p>

CC BY-SA 4.0
2020-11-26T06:36:05.540Z
Will the poisson GLM or Negative GLM account for the covariates as well?
Connor
CC BY-SA 4.0
2020-11-26T08:22:49.663Z
You can use the negative binomial GLM with covariates yes
SPPearce
CC BY-SA 4.0
2020-11-25T10:27:50.783Z
2020-11-25T10:27:50.783Z
Greg
<p>Your assessment is perfectly correct. The easiest approach is to perform a transformation to normalise your data and then you are good to go. DeSeq2 has a standard transformation (I think it is called deseq2) and you can check your results via a Q-Q plot and you are looking for an approximately linear relationship.</p>
<p>There are a multitude of transformations, but the transformation proceedure has been heavily investigated and standardised in RNAseq</p>
<hr />
<p>If normalisation by deseq2 has failed (Q-Q) you simply examine an alternative transformation. However, do not transformation a transformation. If you cannot normalise the data then you are stuck with non-parametric stats in my personal opinion.</p>
<p>However, mixed models can work if you trap the underlying distribution and in a regression will result in a very low residual.</p>

CC BY-SA 4.0
2020-11-26T06:33:28.897Z
Hi Michael, this data has already been normalised by deseq2 so that's why im unsure how to proceed now to analyse it as the qqplots are not normal :(
Connor
CC BY-SA 4.0
2020-11-26T07:45:21.480Z
Commented on above @Connor
M__
CC BY-SA 4.0
2020-11-25T08:20:14.583Z
2020-11-26T08:42:23.670Z
2020-11-26T08:42:23.670Z
M__
<p>I've been handed RNA-seq data with a lot of associated covariates.
This data has been put through the DESeq2 package and as a result normalised the data.
One of the transcripts of interest still has non-normal distribution for two genotypes. This is because many of the normalised counts are now clustered around a value of 4.</p>
<p><a href="https://i.sstatic.net/BBdRf.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BBdRf.png" alt="QQplot of transcript expression against a genotype" /></a></p>
<p>This is making it tricky to perform statistical tests because in order to perform parametric tests (in this case I want to perform a ANCOVA or multiple linear regression to include the covariates) the data must be normally distributed.</p>
<p>I also don't want to use a non-parametric test because I can't include all the covariates.</p>
<p>Should I just perform parametric tests ignoring this violation or do I have to just perform non-parametric on all my data for a fair comparrison?
Or is there another alternative option I am missing?</p>
<p>Thanks in advance</p>

CC BY-SA 4.0
2020-11-25T06:43:44.890Z
The values are normalised counts. Platform is Illumina. This data was put through the DESeq2 package.
Connor
CC BY-SA 4.0
2020-11-26T08:22:25.397Z
Your values of 4 look like they might have been vst transformed. This has a tendency to make non-expressed genes have a value around 4. The vst is in log-space, which you can then see in your plots as they aren't linearly distributed
SPPearce
CC BY-SA 4.0
2020-11-26T16:11:43.320Z
why don't you use the glm model in DESeq2?
StupidWolf
<r><rna-seq>
How would we test for significant changes in expression when many of the normalised counts are clustered together?
# How would we test for significant changes in expression when many of the normalised counts are clustered together?

(Asked by: username_0 on 2020-11-24)

I've been handed RNA-seq data with a lot of associated covariates.
This data has been put through the DESeq2 package and as a result normalised the data.
One of the transcripts of interest still has non-normal distribution for two genotypes. This is because many of the normalised counts are now clustered around a value of 4.

[![QQplot of transcript expression against a genotype](https://i.sstatic.net/BBdRf.png)](https://i.sstatic.net/BBdRf.png)

This is making it tricky to perform statistical tests because in order to perform parametric tests (in this case I want to perform a ANCOVA or multiple linear regression to include the covariates) the data must be normally distributed.

I also don't want to use a non-parametric test because I can't include all the covariates.

Should I just perform parametric tests ignoring this violation or do I have to just perform non-parametric on all my data for a fair comparrison?
Or is there another alternative option I am missing?

Thanks in advance

**Tags:** r, rna-seq
> **Comments:**
> **username_0**: The values are normalised counts. Platform is Illumina. This data was put through the DESeq2 package.  
> **username_1**: Your values of 4 look like they might have been vst transformed. This has a tendency to make non-expressed genes have a value around 4. The vst is in log-space, which you can then see in your plots as they aren't linearly distributed  
> **username_2**: why don't you use the glm model in DESeq2?  

---

## 2 answers

### Answer by username_3 on 2020-11-25. Score: 1

TLDR- just do the analysis in DESeq2 (it can handle multiple covariates)

If I've interpreted your question correctly there seem to be a few confusions here.

First off, for a multiple linear regression aka ANCOVA, one of the assumptions are the *residuals* are normally distributed - however this is almost never an issue in practice. The distribution of the data is not so important, you make the assumption that the data has come from a population which is continuous and normally distributed that's why you've chosen to do an ANCOVA. But, RNAseq produces count data - not continuous and bounded at zero. You need to be looking at using a Poisson GLM, or negative binomial GLM as done by DESeq2 (lookup overdispersion)

The normalisation done by DESeq is to account for different library sizes,it doesn't *transform* the data to make it conform to a normal distribution. You'd need to do some form of data transformation to do that, but don't it's unnecessary.

I'd suggest reading the DESeq vignettes and following their standard pipeline.

Hope that is of some help.

> **Comments:**
> **username_0**: Will the poisson GLM or Negative GLM account for the covariates as well?  
> **username_1**: You can use the negative binomial GLM with covariates yes  

---

### Answer by username_4 on 2020-11-25. Score: 0

Your assessment is perfectly correct. The easiest approach is to perform a transformation to normalise your data and then you are good to go. DeSeq2 has a standard transformation (I think it is called deseq2) and you can check your results via a Q-Q plot and you are looking for an approximately linear relationship.

There are a multitude of transformations, but the transformation proceedure has been heavily investigated and standardised in RNAseq

---

If normalisation by deseq2 has failed (Q-Q) you simply examine an alternative transformation. However, do not transformation a transformation. If you cannot normalise the data then you are stuck with non-parametric stats in my personal opinion.

However, mixed models can work if you trap the underlying distribution and in a regression will result in a very low residual.

> **Comments:**
> **username_0**: Hi Michael, this data has already been normalised by deseq2 so that's why im unsure how to proceed now to analyse it as the qqplots are not normal :(  
> **username_4**: Commented on above @Connor  

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