ti_mfa.RdWill generate a trajectory using MFA.
This method was wrapped inside a container. The original code of this method is available here.
ti_mfa(iter = 2000L, thin = 1L, pc_initialise = 1L, prop_collapse = 0L, scale_input = TRUE, zero_inflation = FALSE)
| iter | Number of MCMC iterations. Domain: U(20, 5000). Default: 2000. Format: integer. |
|---|---|
| thin | MCMC samples to thin. Domain: U(1, 20). Default: 1. Format: integer. |
| pc_initialise | Which principal component to initialise pseudotimes to. Domain: U(1, 5). Default: 1. Format: integer. |
| prop_collapse | Proportion of Gibbs samples which should marginalise over c. Domain: U(0, 1). Default: 0. Format: numeric. |
| scale_input | Logical. If true, input is scaled to have mean 0 variance 1. Default: TRUE. Format: logical. |
| zero_inflation | Logical, should zero inflation be enabled?. Default: FALSE. Format: logical. |
A TI method wrapper to be used together with
infer_trajectory
Campbell, K.R., Yau, C., 2017. Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers. Wellcome Open Research 2, 19.