I am following Seurat vignette: immune_alignment with my own treated vs control data sets.
The vignette offers two methods for identifying differential expressed genes across conditions. One is based on comparing
averaged expression between conditions and the other is based on
FindMarkers which eventually computes fold-change between conditions. The vignette shows many genes common to both methods (per one specific cluster).
In my data sets, the resulting two sets of DE genes are quite different. What could be the reason for this? More generally, what is the core difference between methods?
In my case, for the cluster under examination, the number of cells mapped to control is larger by a factor of 7 compared with those mapped to treatment. Could this be a related factor?