R: Perform factorization for new value of k

optimizeNewK {rliger}

R Documentation

Perform factorization for new value of k

Description

This uses an efficient strategy for updating that takes advantage of the information in the existing factorization. It is most recommended for values of kNew smaller than current value (k, which is set when running runINMF), where it is more likely to speed up the factorization.

Usage

optimizeNewK(
  object,
  kNew,
  lambda = NULL,
  nIteration = 30,
  seed = 1,
  verbose = getOption("ligerVerbose"),
  k.new = kNew,
  max.iters = nIteration,
  rand.seed = seed,
  thresh = NULL
)

Arguments

`object`	A liger object. Should have integrative factorization performed e.g. (`runINMF`) in advance.
`kNew`	Number of factors of factorization.
`lambda`	Numeric regularization parameter. By default `NULL`, this will use the lambda value used in the latest factorization.
`nIteration`	Number of block coordinate descent iterations to perform. Default `30`.
`seed`	Random seed to allow reproducible results. Default `1`. Used by `runINMF` factorization and initialization only when if `kNew` is greater than `k`.
`verbose`	Logical. Whether to show information of the progress. Default `getOption("ligerVerbose")` which is `TRUE` if users have not set.
`k.new`, `max.iters`, `rand.seed`	These arguments are now replaced by others and will be removed in the future. Please see usage for replacement.
`thresh`	Deprecated. New implementation of iNMF does not require a threshold for convergence detection. Setting a large enough `nIteration` will bring it to convergence.

Value

object with W slot updated with the new W matrix, and the H and V slots of each ligerDataset object in the datasets slot updated with the new dataset specific H and V matrix, respectively.

Examples

pbmc <- normalize(pbmc)
pbmc <- selectGenes(pbmc)
pbmc <- scaleNotCenter(pbmc)
# Only running a few iterations for fast examples
if (requireNamespace("RcppPlanc", quietly = TRUE)) {
    pbmc <- runINMF(pbmc, k = 20, nIteration = 2)
    pbmc <- optimizeNewK(pbmc, kNew = 25, nIteration = 2)
}