Inferring a trajectory inference using ouija

Source: R/ti_ouija.R

Will generate a trajectory using ouija.

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

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

Arguments

iter

numeric; Number of iterations (default: 100L; range: from 10L to 1000L)
response_type

discrete; A vector declaring whether each gene exhibits "switch" or "transient"expression. Defaults to "switch" for all genes (default: "switch"; values: "switch", "transient")
inference_type

discrete; 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. (default: "hmc"; values: "hmc", "vb")
normalise_expression

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