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

Description Usage Arguments Details Value Author(s) Examples

Train a SlalomModel to infer model parameters.

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trainSlalom(object, nIterations = 5000, minIterations = 700,
  tolerance = 1e-08, forceIterations = FALSE, shuffle = TRUE,
  pretrain = TRUE, verbose = TRUE, seed = NULL, drop_factors = TRUE)

`object`	a `Rcpp_SlalomModel` object
`nIterations`	integer(1) maximum number of iterations to use in training the model (default: 5000)
`minIterations`	integer(1) minimum number of iterations to perform.
`tolerance`	numeric(1) tolerance to allow between iterations (default 1e-08)
`forceIterations`	logical(1) should the model be forced to update `nIteration` times?
`shuffle`	logical(1) should the order in which factors are updated be shuffled between iterations? Shuffling generally helps speed up convergence so is recommended and defaults is `TRUE`
`pretrain`	logical(1), should the model be "pre-trained" to achieve faster convergence and obtain an initial update order? Recommended; default is `TRUE`
`verbose`	logical(1), should messages be printed about what the function is doing? Default is `TRUE`.
`seed`	integer(1) value supplying a random seed to make results reproducible (default is `NULL`)
`drop_factors`	logical(1), should factors be dropped from the model if the model determines them not to be relevant? Default is `TRUE`.

Train the model using variational Bayes methods to infer parameters.

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)
model <- trainSlalom(model, nIterations = 10)