Inferring a trajectory inference using Projected PAGA

Source: R/ti_projected_paga.R

Will generate a trajectory using Projected PAGA.

This method was wrapped inside a container.

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

Arguments

n_neighbors

integer; Number of neighbours for knn (default: 15L; range: from 1L to 100L)
n_comps

integer; Number of principal components (default: 50L; range: from 0L to 100L)
n_dcs

integer; Number of diffusion components for denoising graph, 0 means no denoising. (default: 15L; range: from 0L to 40L)
resolution

numeric; Resolution of louvain clustering, which determines the granularity of the clustering. Higher values will result in more clusters. (default: 1L; range: from 0.1 to 10L)
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: "fa"; values: "umap", "fa")
run_environment

In which environment to run the method, can be "docker" or "singularity".

Value

A TI method wrapper to be used together with infer_trajectory