<blockquote>
<p>What is &quot;granularity&quot; of the downstream analysis?</p>
</blockquote>
<p>Low granularity implies basic cell types (e.g. T cells, B cells), implies few larger clusters.</p>
<p>High granularity implies more refined cell types (e.g. CD4+ Central Memory T, Treg, Memory B cell etc.), implies more smaller clusters</p>
<blockquote>
<p>How does the parameter &quot;resolution&quot; work?</p>
</blockquote>
<p>A high resolution parameter value produces more smaller clusters, i.e. higher granularity. If you want to know how modularity clustering works, I recommend the Leiden algorithm paper <a href="https://www.nature.com/articles/s41598-019-41695-z" rel="nofollow noreferrer">https://www.nature.com/articles/s41598-019-41695-z</a></p>
<blockquote>
<p>How to know the optimum value for parameter &quot;resolution&quot;?</p>
</blockquote>
<p>Tricky, and it depends on what questions you want to answer. Normally it's a subjective iterative process of trying a clustering resolution, checking if the clusters make sense, re-trying etc. but I also quite like the clustree approach <a href="https://cran.r-project.org/web/packages/clustree/index.html" rel="nofollow noreferrer">https://cran.r-project.org/web/packages/clustree/index.html</a></p>
<blockquote>
<p>The smaller the clusters the more specific cell types we get, is that correct?</p>
</blockquote>
<p>Yes correct</p>

CC BY-SA 4.0
2021-06-30T18:54:47.543Z
2021-07-02T12:59:24.723Z
2021-07-02T12:59:24.723Z
Chris_Rands
<p>I am learning the Seurat algorithms to cluster the scRNA-seq datasets. I found this explanation, but am confused. Can someone explain it to me, &quot;The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells&quot;. My questions are:</p>
<ol>
<li>What is &quot;granularity&quot; of the downstream analysis?</li>
<li>How does the parameter &quot;resolution&quot; work?</li>
<li>How to know the optimum value for parameter &quot;resolution&quot;?</li>
</ol>
<p>I found this paper about the resolution that might help, but not clear to me yet. <a href="https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf" rel="nofollow noreferrer">https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf</a>.</p>
<p>I think the higher the resolution, the more clusters produced meaning the smaller clusters obtained? The smaller the clusters the more specific cell types we get, is that correct?</p>

<scrnaseq><seurat><single-cell><clustering>
Seurat clustering Methods-resolution parameter explanation
# Seurat clustering Methods-resolution parameter explanation

(Asked by: username_0 on 2021-06-30)

I am learning the Seurat algorithms to cluster the scRNA-seq datasets. I found this explanation, but am confused. Can someone explain it to me, "The FindClusters function implements the procedure, and contains a resolution parameter that sets the ‘granularity’ of the downstream clustering, with increased values leading to a greater number of clusters. We find that setting this parameter between 0.6-1.2 typically returns good results for single cell datasets of around 3K cells". My questions are:

1. What is "granularity" of the downstream analysis?
2. How does the parameter "resolution" work?
3. How to know the optimum value for parameter "resolution"?

I found this paper about the resolution that might help, but not clear to me yet. <https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/pdf>.

I think the higher the resolution, the more clusters produced meaning the smaller clusters obtained? The smaller the clusters the more specific cell types we get, is that correct?

**Tags:** scrnaseq, seurat, single-cell, clustering

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## 1 answers

### Answer by username_1 on 2021-06-30. Score: 2

> What is "granularity" of the downstream analysis?

Low granularity implies basic cell types (e.g. T cells, B cells), implies few larger clusters.

High granularity implies more refined cell types (e.g. CD4+ Central Memory T, Treg, Memory B cell etc.), implies more smaller clusters

> How does the parameter "resolution" work?

A high resolution parameter value produces more smaller clusters, i.e. higher granularity. If you want to know how modularity clustering works, I recommend the Leiden algorithm paper <https://www.nature.com/articles/s41598-019-41695-z>

> How to know the optimum value for parameter "resolution"?

Tricky, and it depends on what questions you want to answer. Normally it's a subjective iterative process of trying a clustering resolution, checking if the clusters make sense, re-trying etc. but I also quite like the clustree approach <https://cran.r-project.org/web/packages/clustree/index.html>

> The smaller the clusters the more specific cell types we get, is that correct?

Yes correct


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