Inferring a trajectory inference using DPT

Will generate a trajectory using DPT.

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

ti_dpt(sigma = "local", distance = "euclidean", ndim = 20L,
  density_norm = TRUE, n_local_lower = 5L, n_local_upper = 7L,
  w_width = 0.1, run_environment = NULL)

Arguments

sigma	discrete; Diffusion scale parameter of the Gaussian kernel. A larger sigma might be necessary if the eigenvalues can not be found because of a singularity in the matrix. Must be one of: a numeric global sigma -- a global sigma will be calculated using destiny::find_sigmas() or a destiny::Sigmas-class() object. (default: "local"; values: "local", "global")
distance	discrete; A stats::dist() object, or a character vector specifying which distance metric to use. Allowed measures: cosine distance (1-corr(c_1, c_2)), or rank correlation distance (1-corr(rank(c_1), rank(c_2))) (default: "euclidean"; values: "euclidean", "cosine", "rankcor")
ndim	integer; Number of eigenvectors/dimensions to return (default: 20L; range: from 3L to 100L)
density_norm	logical; Logical. If TRUE, use density normalisation
n_local_lower	integer; If sigma == 'local', the n_local_lower:n_local_upper nearest neighbor(s) determine(s) the local sigma (default: 5L; range: from 2L to 20L)
n_local_upper	integer; See n_local_lower (default: 7L; range: from 2L to 20L)
w_width	numeric; Window width to use for deciding the branch cutoff (default: 0.1; range: from 1e-04 to 1)
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

References

Haghverdi, L., Büttner, M., Wolf, F.A., Buettner, F., Theis, F.J., 2016. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845–848.