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)?