Inferring a trajectory inference using SCORPIUS

Source: R/ti_scorpius.R

Will generate a trajectory using SCORPIUS.

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

ti_scorpius(distance_method = "spearman", ndim = 3L, k = 4L,
  thresh = 0.001, maxit = 10L, stretch = 0,
  smoother = "smooth_spline", sparse = TRUE, run_environment = NULL)

Arguments

distance_method

discrete; A character string indicating which correlationcoefficient (or covariance) is to be computed. One of "pearson", "kendall", or "spearman". (default: "spearman"; values: "spearman", "pearson", "kendall")
ndim

integer; The number of dimensions in the new space. (default: 3L; range: from 2L to 20L)
k

integer; The number of clusters to cluster the data into. (default: 4L; range: from 1L to 20L)
thresh

numeric; principal_curve parameter: convergence threshhold on shortest distances to the curve (default: 0.001; range: from 1e-05 to 1e+05)
maxit

integer; principal_curve parameter: maximum number of iterations (default: 10L; range: from 0L to 50L)
stretch

numeric; principal_curve parameter: a factor by which the curve can be extrapolated when points are projected (default: 0; range: from 0 to 5)
smoother

discrete; principal_curve parameter: choice of smoother (default: "smooth_spline"; values: "smooth_spline", "lowess", "periodic_lowess")
sparse

logical; Whether or not to use sparse MDS dimensionality reduction,for datasets with large amounts of cells.
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

Cannoodt, R., Saelens, W., Sichien, D., Tavernier, S., Janssens, S., Guilliams, M., Lambrecht, B.N., De Preter, K., Saeys, Y., 2016. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development.