Error during trajectory inference

[SebRW]

I'm new to dynverse and I am trying to run my first trajectory analysis.
I created my dataset from a seurat object as followed:

dataset <- wrap_expression(
  expression = t(data.frame(seu@assays[["RNA"]]@data))
  counts = t(data.frame(seu@assays[["RNA"]]@counts))
)

Then added some info:

dataset <- add_prior_information(
  dataset = dataset,
  start_id = naive_id, # a chr vector of my starting cells barcodes
  groups_id = groups_id, # a dataframe of my assigned seurat clusters
  dimred = seu@reductions[["umap"]]@cell.embeddings)

I followed the guidelines and selected "paga" for method

model <- infer_trajectory(dataset, method = "paga", verbose = TRUE)

At the end of the process I obtained the following:
[...]
INFO: Creating SIF file...
/usr/local/lib/python3.7/site-packages/umap/rp_tree.py:450: NumbaWarning:
Compilation is falling back to objec
Error: Error during trajectory inference, see output above

What could possibly went wrong?

R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 19.3
Singularity is at correct version (>=3.0): 3.7.4 is installed
Singularity test successful

[KforKuma]

I met the same problem with docker... The head is like:
/usr/local/lib/python3.7/site-packages/umap/rp_tree.py:450: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "make_euclidean_tree" failed type inference due to: Cannot unify RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none) and RandomProjectionTreeNode(none, bool, array(float32, 1d, C), float64, RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none), RandomProjectionTreeNode(array(int64, 1d, C), bool, none, none, none, none)) for '$14.16', defined at /usr/local/lib/python3.7/site-packages/umap/rp_tree.py (457)