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For the differentially expressed genes analysis, is it possible to check for DEGs based on the levels already identified in the object? For example, my dataset contains cells from 11 subjects, which are marked as so (orig.ident). I want to analyze the differentially expressed markers between them. Will it be accurate to do so? Or should I follow the clustering analysis in Seurat? I want to see the difference in genes between the cells according to the subjects but I'm afraid that may not be the right way to do so.

  head(BC@meta.data)
         orig.ident nCount_RNA nFeature_RNA percent.mito
 BC01_02       BC01   998944.0         9189 0.2387108038
 BC01_03       BC01   999696.4         9548 0.2925345423
 BC01_04       BC01   999057.5         7440 0.0009893724

Is clustering analysis absolutely necessary to see the DEGs between them? or can I directly move to FindMarkers based on the levels (orig.ident)?

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I'm not quite sure what you are asking, but you can use anything in the metadata as a grouping with FindMarkers.

DefaultAssay(combined.all) <- "RNA"
Idents(combined.all) <- "orig.ident"


for (sample in unique(Idents(combined.all))) {
  clustermarkers <- FindMarkers(combined.all, ident.1 = sample)
  print(paste("Cluster", sample))
  print(head(clustermarkers, n = 10))
}
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  • $\begingroup$ thank you for the response! To be clear, when I use different grouping, is it accurate? or is the accuracy dependant on finding markers after clustering? $\endgroup$ Jul 29 '20 at 21:42

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