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 = c(5L, 7L), w_width = 0.1)

Arguments

sigma

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 a character vector -- "local" (default) or "global". Domain: local, global. Default: local. Format: character.
distance

A character vector specifying which distance metric to use. Allowed measures are the Euclidean distance (default), the cosine distance (1-corr(c_1, c_2)), or the rank correlation distance (1-corr(rank(c_1), rank(c_2))). Domain: euclidean, cosine, rankcor. Default: euclidean. Format: character.
ndim

Number of eigenvectors/dimensions to return. Domain: U(3, 100). Default: 20. Format: integer.
density_norm

Logical. If TRUE, use density normalisation. Default: TRUE. Format: logical.
n_local

If sigma == 'local', the n_local nearest neighbor(s) determine(s) the local sigma. Domain: ( U(2, 20), U(2, 20) ). Default: (5, 7). Format: integer_range.
w_width

Window width to use for deciding the branch cutoff. Domain: e^U(-9.21, 0.00). Default: 0.1. Format: numeric.

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.