ti_paga_tree.RdWill generate a trajectory using PAGA Tree. This method runs exactly the same as normal PAGA, but will construct a minimal-spanning tree between clusters
This method was wrapped inside a container. The original code of this method is available here.
ti_paga_tree(n_neighbors = 15L, n_comps = 50L, n_dcs = 15L, resolution = 1L, embedding_type = "fa")
| n_neighbors | Number of neighbours for knn. Domain: U(1, 100). Default: 15. Format: integer. |
|---|---|
| n_comps | Number of principal components. Domain: U(0, 100). Default: 50. Format: integer. |
| n_dcs | Number of diffusion components for denoising graph, 0 means no denoising. Domain: U(0, 40). Default: 15. Format: integer. |
| resolution | Resolution of louvain clustering, which determines the granularity of the clustering. Higher values will result in more clusters. Domain: U(0.1, 10). Default: 1. Format: numeric. |
| embedding_type | Either 'umap' (scales very well, recommended for very large datasets) or 'fa' (ForceAtlas2, often a bit more intuitive for small datasets). Domain: umap, fa. Default: fa. Format: character. |
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.