# How to find differential expressed genes within pseudotime trajectory with Seurat cluster?

Did anyone know how to find the differential expressed genes within pseudotime trajectory with Seurat cluster? In monocle tutorial, we can use BEAM to find the differential expressed genes between the two branches even with the states difference. But how can I find the differential expressed genes with seurat culster enven with the genotype diffence? Thank you so much!

You probably want approaches such as tradeSeq.

Not sure if it helps, but there's a tutorial for how to switch between Seurat and monocle here. The basic approach is to convert it to a cell_data_set object, then process that object with monocle:

erythroid.cds <- as.cell_data_set(erythroid)
erythroid.cds <- cluster_cells(cds = erythroid.cds, reduction_method = "UMAP")
erythroid.cds <- learn_graph(erythroid.cds, use_partition = TRUE)


Pseudotime values can be returned back to the seurat object after processing with monocle via AddMetaData:

erythroid <- AddMetaData(
object = erythroid,
metadata = erythroid.cds@principal_graph_aux@listData$$UMAP$$pseudotime,
col.name = "monocle_pseudotime"
)


Thank you for your reply. So I need to do the code you sent from erythroid.cds <- as.cell_data_set(erythroid) ...to ...col.name = "monocle_pseudotime"), then use the findmarker function of Seurat to find the differential expressed genes? It's that correct?

It's unclear what you're asking, but what you describe here will produce a list of differentially expressed genes based on the cluster definitions of monocle. As far as I'm aware, these clusters don't depend on the pseudotime values.