<p>Assuming you're looking for <em>differential</em> expression values, the <code>FindMarkers</code> function within Seurat would be a good thing to try first:</p>
<pre><code># find all markers distinguishing cluster 5 from clusters 0 and 3
cluster5.markers &lt;- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
head(cluster5.markers, n = 5)
##                       p_val avg_log2FC pct.1 pct.2     p_val_adj
## FCGR3A        8.246578e-205   4.261495 0.975 0.040 1.130936e-200
## IFITM3        1.677613e-195   3.879339 0.975 0.049 2.300678e-191
## CFD           2.401156e-193   3.405492 0.938 0.038 3.292945e-189
## CD68          2.900384e-191   3.020484 0.926 0.035 3.977587e-187
## RP11-290F20.3 2.513244e-186   2.720057 0.840 0.017 3.446663e-182 
</code></pre>
<p>[Note the <code>avg_log2FC</code> column]</p>
<p>More details can be found at the bottom of the Seurat tutorial pages for the PBMC 3K Guided Tutorial, <em>Finding differentially expressed features (cluster biomarkers)</em>:</p>
<p><a href="https://satijalab.org/seurat/articles/pbmc3k_tutorial.html" rel="nofollow noreferrer">https://satijalab.org/seurat/articles/pbmc3k_tutorial.html</a></p>

CC BY-SA 4.0
2023-02-27T00:40:13.630Z
Thanks you very much! Yes, that is what I was looking for. I also found the AverageExpression function to look at expression values across clusters
Ahmed M.
CC BY-SA 4.0
2023-02-24T18:55:13.363Z
2023-02-24T18:55:13.363Z
gringer
<p>I am analyzing publically available scRNA seq datasets on R using Seurat. I have created a seurat object, clustered and annotated different cell types. Now, I am interested in comparing the expression values and percentage of expressing cells within each cluster for a given set of genes I am interested in. I can  visualize their expression using DotPlots, feature plots, etc... but how can I generate a table with the expression values (log2FC) for my defined set of genes across the different clusters/cell types?
Thanks!</p>

CC BY-SA 4.0
2023-02-23T18:39:53.633Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Community
CC BY-SA 4.0
2023-02-24T18:47:31.387Z
Please clarify more precisely what you want, as there is inconsistency in your question. `log2FC` values are log-transformed fold change values, and represent a *change* between two expression profiles, i.e. they represent *differential* expression, not expression.
gringer
<scrnaseq><seurat><gene-expression>
Determine expression values for a defined list of genes across different clusters in scRNA data using seurat
# Determine expression values for a defined list of genes across different clusters in scRNA data using seurat

(Asked by: username_0 on 2023-02-23)

I am analyzing publically available scRNA seq datasets on R using Seurat. I have created a seurat object, clustered and annotated different cell types. Now, I am interested in comparing the expression values and percentage of expressing cells within each cluster for a given set of genes I am interested in. I can visualize their expression using DotPlots, feature plots, etc... but how can I generate a table with the expression values (log2FC) for my defined set of genes across the different clusters/cell types?
Thanks!

**Tags:** scrnaseq, seurat, gene-expression
> **Comments:**
> **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
> **username_2**: Please clarify more precisely what you want, as there is inconsistency in your question. `log2FC` values are log-transformed fold change values, and represent a *change* between two expression profiles, i.e. they represent *differential* expression, not expression.  

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## 1 answers

### Answer by username_2 on 2023-02-24. Score: 1

Assuming you're looking for differential expression values, the FindMarkers function within Seurat would be a good thing to try first:
# find all markers distinguishing cluster 5 from clusters 0 and 3
cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
head(cluster5.markers, n = 5)
##                       p_val avg_log2FC pct.1 pct.2     p_val_adj
## FCGR3A        8.246578e-205   4.261495 0.975 0.040 1.130936e-200
## IFITM3        1.677613e-195   3.879339 0.975 0.049 2.300678e-191
## CFD           2.401156e-193   3.405492 0.938 0.038 3.292945e-189
## CD68          2.900384e-191   3.020484 0.926 0.035 3.977587e-187
## RP11-290F20.3 2.513244e-186   2.720057 0.840 0.017 3.446663e-182 

[Note the avg_log2FC column]
More details can be found at the bottom of the Seurat tutorial pages for the PBMC 3K Guided Tutorial, Finding differentially expressed features (cluster biomarkers):
https://satijalab.org/seurat/articles/pbmc3k_tutorial.html

> **Comments:**
> **username_0**: Thanks you very much! Yes, that is what I was looking for. I also found the AverageExpression function to look at expression values across clusters  

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