text: user2005: 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.
  
  user208: 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.
  
  user678: 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).
  
original_text: 2005: <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>
   208: <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>
   678: <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>
  