text: user4951: 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.
  
  As suggested by the members I have used clustree to check the cluster stability also changes the clustering parameter.
  
  
  user-1: You should browse the web for methods to assess cluster stability. The idea is to permutate clusters by randomly adding and subtracting cells of a given cluster and see whether it collapses or not. I would bet money that many of your clusters are not stable, e.g. 17 and 2. Try to change clustering parameters to decrease resolution in order to get larger clusters. I am not a Seurat user but typically there are resolution and nearest-neighbor parameters, check the documentation. See for example section 10.6 in https://osca.bioconductor.org/clustering.html as well as https://www.biorxiv.org/content/10.1101/2020.05.26.116640v1 for inspiration towards cluster stability assessment.
  
  user3541: 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 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.
  
original_text: 4951: <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.stack.imgur.com/QjGtS.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/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.stack.imgur.com/L18gk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/L18gk.png" alt="enter image description here" /></a></p>
   -1: <p>You should browse the web for methods to assess cluster stability. The idea is to permutate clusters by randomly adding and subtracting cells of a given cluster and see whether it collapses or not. I would bet money that many of your clusters are not stable, e.g. 17 and 2. Try to change clustering parameters to decrease resolution in order to get larger clusters. I am not a Seurat user but typically there are resolution and nearest-neighbor parameters, check the documentation. See for example section 10.6 in <a href="https://osca.bioconductor.org/clustering.html" rel="nofollow noreferrer">https://osca.bioconductor.org/clustering.html</a> as well as <a href="https://www.biorxiv.org/content/10.1101/2020.05.26.116640v1" rel="nofollow noreferrer">https://www.biorxiv.org/content/10.1101/2020.05.26.116640v1</a> for inspiration towards cluster stability assessment.</p>
   3541: <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>
  