<p>I think you are looking to <code>FindAllMarkers</code> function from <code>Seurat</code>. As you said, you just have to define your <code>ident</code>, that have to have the structure of a table (cell names as names and cluster as value):</p>

<pre><code>pident=as.factor(clusters)
names(pident)=cellNames    
object1@ident=pident
</code></pre>

<p>And then run the <code>FindAllMarkers</code> function:</p>

<pre><code>FindAllMarkers(object1, min.pct = 0.25, min.diff.pct = 0.25)
</code></pre>

<p>You can specify several parameters in this function (type of DE to perform, thresholds of expression, etc).</p>

CC BY-SA 4.0
2018-05-08T07:46:49.747Z
2018-05-08T07:46:49.747Z
jxx_fa
<p>Seurat has functions for adding metadata and setting identities. Get unique cell names:</p>

<pre><code>cell.labels &lt;- seuratobject@ident
</code></pre>

<p>Replace column and its name with your cluster labels (<a href="https://bioinformatics.stackexchange.com/a/4303/208">e.g.</a>), then:</p>

<pre><code>seuratobject &lt;- AddMetaData(seuratobject, metadata=cell.labels)
seuratobject &lt;- SetAllIdent(seuratobject, id='yourclusterlabels')
</code></pre>

<p>Because you want to contrast two clusters against each other, I suggest using <code>FindMarkers()</code> as opposed to <code>FindAllMarkers()</code>:</p>

<pre><code>FindMarkers(object, ident.1, ident.2)
</code></pre>

<p>It can also compare combinations of clusters.</p>

CC BY-SA 4.0
2018-05-15T15:49:43.123Z
2018-05-15T16:54:50.990Z
2018-05-15T16:54:50.990Z
Peter
<p>I want to define two clusters of cells in my dataset and find marker genes that are specific to one and the other. Is there a way to do this in <code>Seurat</code>? Say, if I produce two subsets by the <code>SubsetData</code> function, is there a way to feed them into some other function that would calculate marker genes? If not, what other packages would you recommend for doing that?</p>

<p>If you look here:</p>

<p><a href="https://satijalab.org/seurat/seurat_clustering_tutorial_part2.html" rel="nofollow noreferrer">https://satijalab.org/seurat/seurat_clustering_tutorial_part2.html</a></p>

<p>I just need a way to define <code>ident</code> myself, the number of levels (2) and assign numbers to each cell (0, 1), and then run <code>DE</code> between <code>0</code> and <code>1</code> clusters which is obvious how to do afterwards.</p>

CC BY-SA 4.0
2019-03-14T17:31:13.223Z
A related issue was answered on [github](https://github.com/satijalab/seurat/issues/285). Just as an alternative reference to the excellent answers below)
Tapper
<r><single-cell><seurat>
Manually define clusters in Seurat and determine marker genes
# Manually define clusters in Seurat and determine marker genes

(Asked by: username_0 on 2018-05-07)

I want to define two clusters of cells in my dataset and find marker genes that are specific to one and the other. Is there a way to do this in `Seurat`? Say, if I produce two subsets by the `SubsetData` function, is there a way to feed them into some other function that would calculate marker genes? If not, what other packages would you recommend for doing that?

If you look here:

<https://satijalab.org/seurat/seurat_clustering_tutorial_part2.html>

I just need a way to define `ident` myself, the number of levels (2) and assign numbers to each cell (0, 1), and then run `DE` between `0` and `1` clusters which is obvious how to do afterwards.

**Tags:** r, single-cell, seurat
> **Comments:**
> **username_1**: A related issue was answered on [github](https://github.com/satijalab/seurat/issues/285). Just as an alternative reference to the excellent answers below)  

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## 2 answers

### Answer by username_2 on 2018-05-08. Score: 3 | Accepted answer

I think you are looking to FindAllMarkers function from Seurat. As you said, you just have to define your ident, that have to have the structure of a table (cell names as names and cluster as value):
pident=as.factor(clusters)
names(pident)=cellNames    
object1@ident=pident

And then run the FindAllMarkers function:
FindAllMarkers(object1, min.pct = 0.25, min.diff.pct = 0.25)

You can specify several parameters in this function (type of DE to perform, thresholds of expression, etc).


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### Answer by username_3 on 2018-05-15. Score: 3

Seurat has functions for adding metadata and setting identities. Get unique cell names:
cell.labels <- seuratobject@ident

Replace column and its name with your cluster labels (e.g.), then:
seuratobject <- AddMetaData(seuratobject, metadata=cell.labels)
seuratobject <- SetAllIdent(seuratobject, id='yourclusterlabels')

Because you want to contrast two clusters against each other, I suggest using FindMarkers() as opposed to FindAllMarkers():
FindMarkers(object, ident.1, ident.2)

It can also compare combinations of clusters.


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