rliger (version 1.0.1)

optimizeNewK: 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 k smaller than current value, where it is more likely to speed up the factorization.

Usage

optimizeNewK(
  object,
  k.new,
  lambda = NULL,
  thresh = 1e-04,
  max.iters = 100,
  rand.seed = 1,
  verbose = TRUE
)

Value

liger object with H, W, and V slots reset.

Arguments

object

liger object. Should call optimizeALS before calling.

k.new

Inner dimension of factorization (number of factors)

lambda

Regularization parameter. By default, this will use the lambda last used with optimizeALS.

thresh

Convergence threshold. Convergence occurs when |obj0-obj|/(mean(obj0,obj)) < thresh (default 1e-4).

max.iters

Maximum number of block coordinate descent iterations to perform (default 100).

rand.seed

Random seed to set. Only relevant if k.new > k. (default 1)

verbose

Print progress bar/messages (TRUE by default)

Examples

ligerex <- createLiger(list(ctrl = ctrl, stim = stim))
ligerex <- normalize(ligerex)
ligerex <- selectGenes(ligerex)
ligerex <- scaleNotCenter(ligerex)
k <- 5
# Minimum specification for fast example pass
ligerex <- optimizeALS(ligerex, k = k, max.iters = 1)
if (k != 5) {
    ligerex <- optimizeNewK(ligerex, k.new = k, max.iters = 1)
}