<blockquote>
  <p>If the data are single-cell RNA seq, after normalization can we do
  UMAP and Louvain clustering ?</p>
</blockquote>

<p>Typically people run PCA, UMAP and Louvain clustering on the normalised and log-transformed expression counts (but do marker gene and differential expression analysis on the non-normalised values). However, this remains controversial. I recommend reading <a href="https://www.embopress.org/doi/10.15252/msb.20188746" rel="nofollow noreferrer">"Current best practices in single‐cell RNA‐seq analysis: a tutorial"</a>- it's a bit outdated, but still a good reference.</p>

<blockquote>
  <p>By adequate I mean the clusters are the same but some are split into
  two, which makes sens looking at other results (like transcription
  factor analysis).</p>
</blockquote>

<p>The number of (or size of) clusters should not be used to interpret how good the clustering methodology is, since it is generally dependant on the input parameters, e.g. a higher resolution parameter leads to more clusters. Instead, display the clusters on the UMAP and see if they are continuous 'visually' and check what genes are differentially expressed between clusters and see if they make sense 'biologically'.</p>

CC BY-SA 4.0
2020-05-12T09:10:01.763Z
Since we both cited the same paper in our answers, do you think that a paper from 2019 can be called "outdated"? What new developments are not part of this overview?
PPK
CC BY-SA 4.0
2020-05-12T09:18:07.890Z
The single cell field it moving so fast! e.g. Louvain clustering is now replaced by Leiden https://www.nature.com/articles/s41598-019-41695-z
Chris_Rands
CC BY-SA 4.0
2020-05-12T09:31:07.957Z
I agree that there ara lot of developments happening fast (and as a matter of fact that Leiden works better than louvain). However, I think that the main points made by the paper, like the scaling question or how to normalize data are far more enduring and not addressed by any recent development.
PPK
CC BY-SA 4.0
2020-05-12T11:02:05.660Z
Regarding the clustering evaluation bit above, there is plethora of metrics. I always keep an eye on the "average silhouette width", "adjusted rand index" (using SingleR annotations as labels) and also "clustering trees (easily obtained with `clustree`).
haci
CC BY-SA 4.0
2020-05-12T09:03:56.097Z
2020-05-12T09:03:56.097Z
Chris_Rands
<p>There is <a href="https://www.embopress.org/doi/10.15252/msb.20188746" rel="nofollow noreferrer">discussion</a> if scaling the data (making the data range for all genes the same) is something you need for single cell data. The argument is that the expression differences between genes themselves are informative. Normalization on the other hand is always necessary.<br>
The big single cell pipelines like <code>Seurat</code> or <code>Monocle</code> use both normalization and scaling as standard.</p>

<p>If you would like some of the clusters you get split into subclusters you could either increase the resolution parameter that the clustering functions usually have. Or you can take the cells from a cluster, then subcluster them and add the subclusters to the original clustering results.</p>

<p>For yor next question it would be great if you could provide the plots of the data because this way it is easier to understand what the problem is. </p>

CC BY-SA 4.0
2020-05-12T09:06:14.970Z
2020-05-12T09:06:14.970Z
PPK
<p>If you working on known cell culture, I recommend using classification approach instead of clustering. One library that I used to publish my paper is SingleR. I checked the expression x cell type matrix by both approaches (Seurat Clustering and SingleR), the classification approach make much more sense.</p>

CC BY-SA 4.0
2020-05-15T03:34:19.113Z
2020-05-15T03:34:19.113Z
Thanh Nguyen
<p>I know that scaled data must be used for PCA for example as it is based on variance maximization.</p>

<p>However I'm wondering if it's the same case for UMAP ? If the data are single-cell RNA seq, after normalization can we do UMAP and Louvain clustering ? </p>

<p>I tried both and get similar results, however the Louvain clustering seems to be more adequate on normalized data than on scaled data. </p>

<p>By adequate I mean the clusters are the same but some are split into two, which makes sens looking at other results (like transcription factor analysis). </p>

CC BY-SA 4.0
2020-05-11T18:34:48.110Z
sorry what is the difference between scaled data and normalized data?
StupidWolf
<scrnaseq><single-cell><umap>
umap and Louvain clustering on normalized data
# umap and Louvain clustering on normalized data

(Asked by: username_0 on 2020-05-11)

I know that scaled data must be used for PCA for example as it is based on variance maximization.

However I'm wondering if it's the same case for UMAP ? If the data are single-cell RNA seq, after normalization can we do UMAP and Louvain clustering ?

I tried both and get similar results, however the Louvain clustering seems to be more adequate on normalized data than on scaled data.

By adequate I mean the clusters are the same but some are split into two, which makes sens looking at other results (like transcription factor analysis).

**Tags:** scrnaseq, single-cell, umap
> **Comments:**
> **username_1**: sorry what is the difference between scaled data and normalized data?  

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## 3 answers

### Answer by username_2 on 2020-05-12. Score: 3

> If the data are single-cell RNA seq, after normalization can we do
> UMAP and Louvain clustering ?

Typically people run PCA, UMAP and Louvain clustering on the normalised and log-transformed expression counts (but do marker gene and differential expression analysis on the non-normalised values). However, this remains controversial. I recommend reading ["Current best practices in single‐cell RNA‐seq analysis: a tutorial"](https://www.embopress.org/doi/10.15252/msb.20188746)- it's a bit outdated, but still a good reference.

> By adequate I mean the clusters are the same but some are split into
> two, which makes sens looking at other results (like transcription
> factor analysis).

The number of (or size of) clusters should not be used to interpret how good the clustering methodology is, since it is generally dependant on the input parameters, e.g. a higher resolution parameter leads to more clusters. Instead, display the clusters on the UMAP and see if they are continuous 'visually' and check what genes are differentially expressed between clusters and see if they make sense 'biologically'.

> **Comments:**
> **username_3**: Since we both cited the same paper in our answers, do you think that a paper from 2019 can be called "outdated"? What new developments are not part of this overview?  
> **username_2**: The single cell field it moving so fast! e.g. Louvain clustering is now replaced by Leiden https://www.nature.com/articles/s41598-019-41695-z  
> **username_3**: I agree that there ara lot of developments happening fast (and as a matter of fact that Leiden works better than louvain). However, I think that the main points made by the paper, like the scaling question or how to normalize data are far more enduring and not addressed by any recent development.  
> **username_4**: Regarding the clustering evaluation bit above, there is plethora of metrics. I always keep an eye on the "average silhouette width", "adjusted rand index" (using SingleR annotations as labels) and also "clustering trees (easily obtained with `clustree`).  

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### Answer by username_3 on 2020-05-12. Score: 2

There is [discussion](https://www.embopress.org/doi/10.15252/msb.20188746) if scaling the data (making the data range for all genes the same) is something you need for single cell data. The argument is that the expression differences between genes themselves are informative. Normalization on the other hand is always necessary.  
The big single cell pipelines like `Seurat` or `Monocle` use both normalization and scaling as standard.

If you would like some of the clusters you get split into subclusters you could either increase the resolution parameter that the clustering functions usually have. Or you can take the cells from a cluster, then subcluster them and add the subclusters to the original clustering results.

For yor next question it would be great if you could provide the plots of the data because this way it is easier to understand what the problem is.


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### Answer by username_5 on 2020-05-15. Score: 0

If you working on known cell culture, I recommend using classification approach instead of clustering. One library that I used to publish my paper is SingleR. I checked the expression x cell type matrix by both approaches (Seurat Clustering and SingleR), the classification approach make much more sense.


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