Inferring a trajectory inference using Embeddr

Will generate a trajectory using Embeddr.

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

ti_embeddr(ndim = 2L, kernel = "nn", metric = "correlation",
  nn_pct = 0, eps = 0, t = 0, symmetrize = "mean",
  measure_type = "unorm", thresh = 0.001, maxit = 10L, stretch = 2,
  smoother = "smooth.spline", run_environment = NULL)

Arguments

ndim	integer; Dimension of the embedded space, default is 2 (default: 2L; range: from 2L to 10L)
kernel	discrete; The choice of kernel. 'nn' will give nearest neighbours, 'dist' gives minimum distance and'heat' gives a heat kernel. Discussed in detail in 'Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering',Belkin & Niyogi (default: "nn"; values: "nn", "dist", "heat")
metric	discrete; The metric with which to assess 'closeness' for nearest neighbour selection, one of'correlation' (pearson) or 'euclidean'. Default is 'correlation'. (default: "correlation"; values: "correlation", "euclidean", "cosine")
nn_pct	numeric; The percentage of cells to use as tge number of nearest neighbours if kernel == 'nn'. (default: 0; range: from -2 to 1)
eps	numeric; Maximum distance parameter if kernel == 'dist' (default: 0; range: from -5 to 5)
t	numeric; 'time' for heat kernel if kernel == 'heat' (default: 0; range: from -5 to 5)
symmetrize	discrete; How to make the adjacency matrix symmetric. Note that slightlycounterintuitively, node i having node j as a nearest neighbour doesn't guarantee nodej has node i. There are several ways to get round this: ceil If the above case occurs set the link weight to 1 (ie take the ceiling of the mean case) floor If the above case occurs set the link weight to 0 (ie take the floor of the mean case) (default: "mean"; values: "mean", "ceil", "floor")
measure_type	discrete; Type of laplacian eigenmap, which corresponds to the constraint on the eigenvalue problem. Iftype is 'unorm' (default), then the graph measure used is the identity matrix, while if type is 'norm' then the measureused is the degree matrix. (default: "unorm"; values: "unorm", "norm")
thresh	numeric; Convergence threshold on shortest distances to the curve. (default: 0.001; range: from 1e-05 to 1e+05)
maxit	integer; Maximum number of iterations. (default: 10L; range: from 0L to 50L)
stretch	numeric; A factor by which the curve can be extrapolated whenpoints are projected. Default is 2 (times the last segmentlength). The default is 0 for smoother equal to"periodic_lowess". (default: 2; range: from 0 to 5)
smoother	discrete; Choice of smoother. The default is"smooth_spline", and other choices are "lowess" and"periodic_lowess". The latter allows one to fit closed curves.Beware, you may want to use iter = 0 with lowess(). (default: "smooth.spline"; values: "smooth.spline", "lowess", "periodic.lowess")
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

Campbell, K., Ponting, C.P., Webber, C., 2015. Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles.