Will generate a trajectory using PAGA.
This method was wrapped inside a container. The original code of this method is available here.
ti_paga(n_neighbors = 15L, n_comps = 50L, n_dcs = 15L, resolution = 1L, embedding_type = "fa", run_environment = NULL)
| n_neighbors | integer; Number of neighbours for knn (default: |
|---|---|
| n_comps | integer; Number of principal components (default: |
| n_dcs | integer; Number of diffusion components for denoising graph, 0
means no denoising. (default: |
| resolution | numeric; Resolution of louvain clustering, which determines
the granularity of the clustering. Higher values will result in more clusters.
(default: |
| embedding_type | discrete; Either 'umap' (scales very well, recommended
for very large datasets) or 'fa' (ForceAtlas2, often a bit more intuitive for
small datasets). (default: |
| run_environment | In which environment to run the method, can be |
A TI method wrapper to be used together with
infer_trajectory
Wolf, F.A., Hamey, F., Plass, M., Solana, J., Dahlin, J.S., Gottgens, B., Rajewsky, N., Simon, L., Theis, F.J., 2017. Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.