ti_paga_tree: PAGA Tree

Description Usage Arguments Value References

Description

Will 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.

Usage

1
2
3
ti_paga_tree(filter_features = TRUE, n_neighbors = 15L,
  n_comps = 50L, n_dcs = 15L, resolution = 1L,
  embedding_type = "fa")

Arguments

filter_features

Whether to do feature filtering. Default: TRUE. Format: logical.

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.

Value

A TI method wrapper to be used together with infer_trajectory

References

Wolf, F.A., Hamey, F.K., Plass, M., Solana, J., Dahlin, J.S., Göttgens, B., Rajewsky, N., Simon, L., Theis, F.J., 2019. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biology 20.


dynverse/dynmethods documentation built on July 6, 2019, 11:30 a.m.