URD

Will generate a trajectory using URD.

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

ti_urd(knn = 0L, sigma.use = 0, distance = "euclidean",
  n_floods = 20L, stability.div = 10L, mp.factor = 1L,
  perplexity = 30L, theta = 0.5, max_iter = 1000L, num.nn = 30L,
  do.jaccard = TRUE, optimal.cells.forward = 20L,
  max.cells.back = 40L, n.per.tip = 25000L, root.visits = 1L,
  max.steps = 25000L, n.subsample = 10L, divergence.method = "ks",
  cells.per.pseudotime.bin = 80L, bins.per.pseudotime.window = 5L,
  p.thresh = 0.01)

Arguments

knn

Number of nearest neighbors to use. 0 takes a guess. Domain: U(0, 50). Default: 0. Format: integer.
sigma.use

Kernel width to use for the diffusion map. 0 uses destiny's global auto-detection procedure. Domain: U(0, 1). Default: 0. Format: numeric.
distance

Distance metric to use for determining transition probabilities. Domain: euclidean, cosine, rankcor. Default: euclidean. Format: character.
n_floods

Number of simulations to perform and average. Domain: U(5, 50). Default: 20. Format: integer.
stability.div

Number of simulation subsamplings to calculate. Domain: U(2, 50). Default: 10. Format: numeric.
mp.factor

Retain PCs than are this factor more than the estimated maximum singular value expected or random data. This is useful in cases when there are many PCs that have standard deviations just above that expected by random, which probably represent noise and should be excluded. Domain: U(0, 10). Default: 1. Format: numeric.
perplexity

Perplexity parameter for the tSNE. Domain: U(0, 100). Default: 30. Format: numeric.
theta

Speed/accuracy trade-off for Barnes-Hut approximation of tSNE. 0 is exact tSNE, higher is less accurate. Domain: U(0, 1). Default: 0.5. Format: numeric.
max_iter

Number of nearest neighbors to use. 0 takes a guess. Domain: e^U(4.61, 9.21). Default: 1000. Format: integer.
num.nn

How many nearest-neighbors to use in the k-nn graph. Domain: e^U(2.30, 4.61). Default: 30. Format: integer.
do.jaccard

Weight edges in the k-nn graph according to their Jaccard overlap?. Default: TRUE. Format: logical.
optimal.cells.forward

The number of cells in the direction specified by pseudotime.direction at which the logistic should reach 1-asymptote. Domain: e^U(1.61, 4.61). Default: 20. Format: numeric.
max.cells.back

The number of cells in the direction opposite from that specified by pseudotime.direction at which the logistic should reach asymptote. Domain: e^U(1.61, 5.30). Default: 40. Format: numeric.
n.per.tip

Number of walks to do per tip. Domain: e^U(4.61, 13.82). Default: 25000. Format: integer.
root.visits

Number of steps to take that visit a root.cell before stopping. Domain: U(1, 5). Default: 1. Format: integer.
max.steps

Number of walks to do per tip. Domain: e^U(4.61, 13.82). Default: 25000. Format: integer.
n.subsample

Number of subsamplings to perform for calculating stability. Domain: e^U(0.69, 4.61). Default: 10. Format: integer.
divergence.method

Distance metric to use for determining transition probabilities. Domain: ks, preference. Default: ks. Format: character.
cells.per.pseudotime.bin

Approximate number of cells to assign to each pseudotime bin for branchpoint finding. Domain: e^U(2.30, 6.91). Default: 80. Format: integer.
bins.per.pseudotime.window

Width of moving window in pseudotime used for branchpoint finding, in terms of bins. Domain: U(2, 20). Default: 5. Format: integer.
p.thresh

P-value threshold to use in determining whether visitation is significantly different from pairs of tips. Domain: e^U(-11.51, 0.00). Default: 0.01. Format: numeric.

Value

A TI method wrapper to be used together with infer_trajectory

References

Farrell, J.A., Wang, Y., Riesenfeld, S.J., Shekhar, K., Regev, A., Schier, A.F., 2018. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131.