<p>I wouldn't be surprised if the 15 cell types that you are expecting were mostly characterized via &quot;protein-based&quot; methods; antibody staining, flow cytometry using fluorophore coupled antibodies, ... The data that scRNA-seq is RNA based and should not be expected to provide the same information as protein based assays. Moreover, the scRNA-seq data, especially those from microfluidic systems like 10x, are sparse and might not be enough to resolve closely related cell types/states such as those of T cells.</p>
<p>On top of the aforementioned, evaluating clusters or clustering stability is a difficult task and in my experience, quite some cluster evaluation metrics cannot be easily applied to single cell data simply because of the shear size of the data. Having asked your question and looked for answers (and I am still doing that), I am doing the following:</p>
<p>i) Try to use transcriptomic markers as much as possible. Even then a lack of a marker would not mean much in terms of scRNA-seq, it might very well not be detected simply due to sensitivity</p>
<p>ii) Use <a href="https://cran.r-project.org/web/packages/clustree/index.html" rel="nofollow noreferrer">clustree</a> do select the most plausible number of clusters (resolution parameter in Seurat). The package is compatible with Seurat and some other scRNA-seq packages.</p>
<p>iii) Use silhouette width as a metric for clustering. This is computationally expensive, however, should be fine for your 5000 cells. For larger cell numbers, I use another package that approximates this (will add a link but first I have go through my scripts for the name).</p>
<p>iv) Check if batch effects are accounting for what you call &quot;sub-clusters&quot;. For example clusters 0, 14, 24 and 25 above might correspond to different samples processed in different days.</p>

CC BY-SA 4.0
2020-09-30T23:12:28.937Z
@haci Thanks for such a wonderful explanation. I have just added the clustree plot. Its dont look that great; but would you please provide your comment? Thanks
user3377241
CC BY-SA 4.0
2020-09-29T10:22:03.017Z
2020-09-29T10:31:49.230Z
2020-09-29T10:31:49.230Z
haci
<p>I am trying to run seurat on ~5000 sinngle cells. I am expecting minimum 15 cell types to be present in the data. I tried to runn it with multiple conditions;I can see there is 27 clusters; I believe many of them might be sub clusters. Is there a way to know which clusters are actually a sub-clusters.</p>
<p><a href="https://i.sstatic.net/QjGtS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/QjGtS.png" alt="enter image description here" /></a></p>
<p>As suggested by the members I have used clustree to check the cluster stability also changes the clustering parameter.</p>
<p><a href="https://i.sstatic.net/L18gk.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/L18gk.png" alt="enter image description here" /></a></p>

<seurat><single-cell>
Single cell RNAseq cell cluster (true cluster or sub cluster)
# Single cell RNAseq cell cluster (true cluster or sub cluster)

(Asked by: username_0 on 2020-09-28)

I am trying to run seurat on ~5000 sinngle cells. I am expecting minimum 15 cell types to be present in the data. I tried to runn it with multiple conditions;I can see there is 27 clusters; I believe many of them might be sub clusters. Is there a way to know which clusters are actually a sub-clusters.

[![enter image description here](https://i.sstatic.net/QjGtS.png)](https://i.sstatic.net/QjGtS.png)

As suggested by the members I have used clustree to check the cluster stability also changes the clustering parameter.

[![enter image description here](https://i.sstatic.net/L18gk.png)](https://i.sstatic.net/L18gk.png)

**Tags:** seurat, single-cell

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## 1 answers

### Answer by username_1 on 2020-09-29. Score: 2 | Accepted answer

I wouldn't be surprised if the 15 cell types that you are expecting were mostly characterized via "protein-based" methods; antibody staining, flow cytometry using fluorophore coupled antibodies, ... The data that scRNA-seq is RNA based and should not be expected to provide the same information as protein based assays. Moreover, the scRNA-seq data, especially those from microfluidic systems like 10x, are sparse and might not be enough to resolve closely related cell types/states such as those of T cells.

On top of the aforementioned, evaluating clusters or clustering stability is a difficult task and in my experience, quite some cluster evaluation metrics cannot be easily applied to single cell data simply because of the shear size of the data. Having asked your question and looked for answers (and I am still doing that), I am doing the following:

i) Try to use transcriptomic markers as much as possible. Even then a lack of a marker would not mean much in terms of scRNA-seq, it might very well not be detected simply due to sensitivity

ii) Use [clustree](https://cran.r-project.org/web/packages/clustree/index.html) do select the most plausible number of clusters (resolution parameter in Seurat). The package is compatible with Seurat and some other scRNA-seq packages.

iii) Use silhouette width as a metric for clustering. This is computationally expensive, however, should be fine for your 5000 cells. For larger cell numbers, I use another package that approximates this (will add a link but first I have go through my scripts for the name).

iv) Check if batch effects are accounting for what you call "sub-clusters". For example clusters 0, 14, 24 and 25 above might correspond to different samples processed in different days.

> **Comments:**
> **username_0**: @haci Thanks for such a wonderful explanation. I have just added the clustree plot. Its dont look that great; but would you please provide your comment? Thanks  

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