<p>Typically some sort of variance stabilizing transform is used before clustering. Popular options are regularized log transformation or a vst transform, which are available in DESeq2. Note that these are NOT used for performing differential expression, just things like clustering and PCA. For differential expression one would use TMM, RLE or similar.</p>

CC BY-SA 4.0
2020-05-21T14:56:41.593Z
Thank you for your comment. Yes, I would like to calculate the distance to perform clustering, not differential expression.
user224050
CC BY-SA 4.0
2020-05-21T15:01:20.743Z
So if we want to clustering after calculating distance, you mean we should use regularized log transformation or a vst transform. And when we want to perform differential gene expression analysis, we should use TMM, RLE. So we need to change normalization methods in one paper depending on analytic plan, right??
user224050
CC BY-SA 4.0
2020-05-21T15:10:43.860Z
Yes, though regularized log and such are transforms and not normalizations.
Devon Ryan
CC BY-SA 4.0
2020-05-22T16:01:08.517Z
So for the analytic pipeline to compute the distance, we should not use both normalization and transformation ,right?  raw count (without transformation)→vst or other transformation.
https://towardsdatascience.com/pitfalls-of-data-normalization-bf05d65f1f4c
user224050
CC BY-SA 4.0
2020-05-22T19:40:59.693Z
Please see and follow the DEseq2 tutorial, this is covered there.
Devon Ryan
CC BY-SA 4.0
2020-05-21T13:53:36.113Z
2020-05-21T13:53:36.113Z
Devon Ryan
<p>I have been working on clustering using RNAseq data. To compute distance, what kinds of normalization is optimal?</p>

<p>Can we use normalization using relative log normalization? I understand this should be used for differential gene expression analysis, not for unsupervised learning in which we do not use information of disease condition.</p>

<p>If you can show good papers, please share.</p>

CC BY-SA 4.0
2020-05-21T06:19:49.190Z
Hi it would be useful to describe the disease in question and some idea of the data. "Normalisation" is often used in parametric statistics to convert data to a normal distribution as a pre-requisite of the analysis, or linear modelling. Transformation is a "hit and miss" business in general. Unsupervised learning ... it depends what you mean, but you wouldn't do this for PCA or tSNE. More details please.
M__
<rna><clustering>
about the normalization of RNAseq data for calculating distance for unsupervised learning
# about the normalization of RNAseq data for calculating distance for unsupervised learning

(Asked by: username_0 on 2020-05-21)

I have been working on clustering using RNAseq data. To compute distance, what kinds of normalization is optimal?

Can we use normalization using relative log normalization? I understand this should be used for differential gene expression analysis, not for unsupervised learning in which we do not use information of disease condition.

If you can show good papers, please share.

**Tags:** rna, clustering
> **Comments:**
> **username_1**: Hi it would be useful to describe the disease in question and some idea of the data. "Normalisation" is often used in parametric statistics to convert data to a normal distribution as a pre-requisite of the analysis, or linear modelling. Transformation is a "hit and miss" business in general. Unsupervised learning ... it depends what you mean, but you wouldn't do this for PCA or tSNE. More details please.  

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## 1 answers

### Answer by username_2 on 2020-05-21. Score: 2

Typically some sort of variance stabilizing transform is used before clustering. Popular options are regularized log transformation or a vst transform, which are available in DESeq2. Note that these are NOT used for performing differential expression, just things like clustering and PCA. For differential expression one would use TMM, RLE or similar.

> **Comments:**
> **username_0**: Thank you for your comment. Yes, I would like to calculate the distance to perform clustering, not differential expression.  
> **username_0**: So if we want to clustering after calculating distance, you mean we should use regularized log transformation or a vst transform. And when we want to perform differential gene expression analysis, we should use TMM, RLE. So we need to change normalization methods in one paper depending on analytic plan, right??  
> **username_2**: Yes, though regularized log and such are transforms and not normalizations.  
> **username_0**: So for the analytic pipeline to compute the distance, we should not use both normalization and transformation ,right?  raw count (without transformation)→vst or other transformation. https://towardsdatascience.com/pitfalls-of-data-normalization-bf05d65f1f4c  
> **username_2**: Please see and follow the DEseq2 tutorial, this is covered there.  

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