<p>No Seurat function is needed. In the seurat object &quot;cells&quot;, you can access the raw counts or normalized counts using <code>cells@assays$RNA@counts</code> (raw counts) or <code>cells@assays$SCT@data</code> (SCT normalized data)</p>

CC BY-SA 4.0
2022-06-06T22:25:35.370Z
2022-06-06T22:25:35.370Z
mmpp
<p>I'm currently working with a seurat object and I'd like to calculate the expression values per gene for all cells within a particular cluster.</p>
<p>I've gone ahead and subsetted the cluster of interest.</p>
<pre><code>tumor &lt;- subset(x = data, idents = c(&quot;Tumor&quot;), invert = FALSE)
</code></pre>
<p>How would I calculate the expression values per gene per cell?</p>
<pre><code>FindAllMarkers
</code></pre>
<p>Finds markers (differentially expressed genes) for each of the identity classes in a dataset per ident.</p>

<r><scrnaseq><seurat><gene-expression>
how to calculate the expression values per gene for all cells
# how to calculate the expression values per gene for all cells

(Asked by: username_0 on 2022-06-03)

I'm currently working with a seurat object and I'd like to calculate the expression values per gene for all cells within a particular cluster.
I've gone ahead and subsetted the cluster of interest.
tumor <- subset(x = data, idents = c("Tumor"), invert = FALSE)

How would I calculate the expression values per gene per cell?
FindAllMarkers

Finds markers (differentially expressed genes) for each of the identity classes in a dataset per ident.

**Tags:** r, scrnaseq, seurat, gene-expression

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## 1 answers

### Answer by username_0 on 2022-06-06. Score: 2 | Accepted answer

No Seurat function is needed. In the seurat object "cells", you can access the raw counts or normalized counts using `cells@assays$RNA@counts` (raw counts) or `cells@assays$SCT@data` (SCT normalized data)


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