ti_pseudogp: pseudogp

Description Usage Arguments Value References

Description

Will generate a trajectory using pseudogp.

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

Usage

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ti_pseudogp(smoothing_alpha = 10, smoothing_beta = 3,
  pseudotime_mean = 0.5, pseudotime_var = 1, chains = 3L,
  iter = 100L, dimreds = c("pca", "mds"), initialise_from = "random")

Arguments

smoothing_alpha

The hyperparameter for the Gamma distribution that controls arc-length. Domain: U(1, 20). Default: 10. Format: numeric.

smoothing_beta

The hyperparameter for the Gamma distribution that controls arc-length. Domain: U(1, 20). Default: 3. Format: numeric.

pseudotime_mean

The mean of the constrained normal prior on the pseudotimes. Domain: U(0, 1). Default: 0.5. Format: numeric.

pseudotime_var

The variance of the constrained normal prior on the pseudotimes. Domain: U(0.01, 1). Default: 1. Format: numeric.

chains

The number of chains for the MCMC trace. Domain: U(1, 20). Default: 3. Format: integer.

iter

The number of iterations for the MCMC trace. Domain: e^U(4.61, 6.91). Default: 100. Format: integer.

dimreds

A character vector specifying which dimensionality reduction methods to use. See dyndimred::dimred() for the list of available dimensionality reduction methods. Domain: all subsets of pca, mds, tsne, ica, lle, landmark_mds, mds_sammon, mds_isomds, mds_smacof, umap, dm_diffusionmap, dm_destiny. Default: pca, mds. Format: subset.

initialise_from

How to initialise the MCMC chain. One of "random" (stan decides),"principal_curve", or "pca" (the first component of PCA rescaled is taken to be the pseudotimes).Note: if multiple representations are provided, pseudogp will take the principal curve orpca from the first rather than combining them. If a particular representation is required, it isup to the user to re-order them. Domain: random, principal_curve, pca. Default: random. Format: character.

Value

A TI method wrapper to be used together with infer_trajectory

References

Campbell, K.R., Yau, C., 2016. Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference. PLOS Computational Biology 12, e1005212.


dynverse/dynmethods documentation built on July 6, 2019, 11:30 a.m.