ti_ouija: ouija

In dynverse/dynmethods: A collection of trajectory inference methods

ouija

Description

Will generate a trajectory using ouija.

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

Usage

ti_ouija(
  iter = 100L,
  response_type = "switch",
  inference_type = "hmc",
  normalise_expression = TRUE
)

Arguments

iter	Number of iterations. Domain: e^U(2.30, 6.91). Default: 100. Format: numeric.
response_type	A vector declaring whether each gene exhibits "switch" or "transient"expression. Defaults to "switch" for all genes. Domain: switch, transient. Default: switch. Format: character.
inference_type	The type of inference to be performed, either hmc for HamiltonianMonte Carlo or vb for ADVI (Variational Bayes). Note that HMC is typically more accuratebut VB will be orders of magnitude faster. Domain: hmc, vb. Default: hmc. Format: character.
normalise_expression	Logical, default TRUE. If TRUE the data is pre-normalisedso the average peak expression is approximately 1. This makes the strength parametersapproximately comparable between genes. Default: TRUE. Format: logical.

Value

A TI method wrapper to be used together with infer_trajectory

References

Campbell, K.R., Yau, C., 2016. A descriptive marker gene approach to single-cell pseudotime inference.

dynverse/dynmethods documentation built on Jan. 18, 2024, 4:44 a.m.