initSlalom: Initialize a SlalomModel object
In PMBio/slalom: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data

Description Usage Arguments Details Value Author(s) Examples

Initialize a SlalomModel with sensible starting values for parameters before training the model.

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initSlalom(object, alpha_priors = NULL, epsilon_priors = NULL,
  noise_model = "gauss", seed = NULL, pi_prior = NULL, n_hidden = NULL,
  design = NULL, verbose = FALSE, save_init = FALSE)

`object`	a `Rcpp_SlalomModel` object
`alpha_priors`	numeric(2) giving alpha and beta hyperparameters for a gamma prior distribution for alpha parameters (precision of factor weights)
`epsilon_priors`	numeric(2) giving alpha and beta hyperparameters for a gamma prior distribution for noise precision parameters
`noise_model`	character(1) defining noise model, defaults to "gauss" for Gaussian noise model
`seed`	integer(1) value supplying a random seed to make results reproducible (default is `NULL`)
`pi_prior`	numeric matrix (genes x factors) giving prior probability of a gene being active for a factor
`n_hidden`	integer(1), number of hidden factors in model. Required if `pi_prior` is not `NULL`, ignored otherwise.
`design`	matrix of known factors (covariates) to fit in the model. Optional if `pi_prior` is not `NULL`, ignored otherwise.
`verbose`	logical(1), should messages be printed about what the function is doing? Default is `TRUE`.
`save_init`	logical(1), save the initial X values (factor states for each cell) in the object? Default is `FALSE` as this is potentially a large N (number of cell) by K (number of factors) matrix.

It is strongly recommended to use newSlalomModel to create the SlalomModel object prior to applying initSlalom.

an 'Rcpp_SlalomModel' object

Davis McCarthy

gmtfile <- system.file("extdata", "reactome_subset.gmt", package = "slalom")
genesets <- GSEABase::getGmt(gmtfile)
data("mesc")
model <- newSlalomModel(mesc, genesets, n_hidden = 5, min_genes = 10)
model <- initSlalom(model)