Projected PAGA

Will generate a trajectory using Projected PAGA.

This method was wrapped inside a container. The original code of this method is available here.

ti_projected_paga(n_neighbors = 15L, n_comps = 50L, n_dcs = 15L,
  resolution = 1L, embedding_type = "fa", connectivity_cutoff = 0.05)



Number of neighbours for knn. Domain: U(1, 100). Default: 15. Format: integer.


Number of principal components. Domain: U(0, 100). Default: 50. Format: integer.


Number of diffusion components for denoising graph, 0 means no denoising. Domain: U(0, 40). Default: 15. Format: integer.


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.


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.


Cutoff for the connectivity matrix. Domain: U(0, 1). Default: 0.05. Format: numeric.


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.