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Perform factorization for new value of k

Source: R/optimizeNewParam.R
optimizeNewK.Rd

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.

See also

runINMF, optimizeNewLambda, optimizeNewData

Examples

pbmc <- normalize(pbmc)
#> ℹ Normalizing datasets "ctrl"
#> ℹ Normalizing datasets "stim"
#> ✔ Normalizing datasets "stim" ... done
#> 
#> ℹ Normalizing datasets "ctrl"

#> ✔ Normalizing datasets "ctrl" ... done
#> 
pbmc <- selectGenes(pbmc)
#> ℹ Selecting variable features for dataset "ctrl"
#> ✔ ... 168 features selected out of 249 shared features.
#> ℹ Selecting variable features for dataset "stim"
#> ✔ ... 166 features selected out of 249 shared features.
#> ✔ Finally 173 shared variable features are selected.
pbmc <- scaleNotCenter(pbmc)
#> ℹ Scaling dataset "ctrl"
#> ✔ Scaling dataset "ctrl" ... done
#> 
#> ℹ Scaling dataset "stim"
#> ✔ Scaling dataset "stim" ... done
#> 
# 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)
}
#> Fri Oct 25 15:12:33 2024 ... Initializing with new k...