rliger (version 2.0.1)

optimizeSubset: Perform factorization for subset of data

Description

Uses an efficient strategy for updating that takes advantage of the information in the existing factorization.

Usage

optimizeSubset(
  object,
  clusterVar = NULL,
  useClusters = NULL,
  lambda = NULL,
  nIteration = 30,
  cellIdx = NULL,
  scaleDatasets = NULL,
  seed = 1,
  verbose = getOption("ligerVerbose"),
  cell.subset = cellIdx,
  cluster.subset = useClusters,
  max.iters = nIteration,
  datasets.scale = scaleDatasets,
  thresh = NULL
)

Value

Subset object with factorization matrices optimized, including the W matrix in liger object, and W and V

matrices in each ligerDataset object in the datasets

slot. scaleData in the ligerDataset objects of datasets specified by scaleDatasets will also be updated to reflect the subset.

Arguments

object

liger object. Should have integrative factorization (e.g. runINMF) performed in advance.

clusterVar, useClusters

Together select the clusters to subset the object conveniently. clusterVar is the name of variable in cellMeta(object) and useClusters should be vector of names of clusters in the variable. clusterVar is by default the default cluster (See runCluster, or defaultCluster at "Cell metadata access"). Users can otherwise select cells explicitly with cellIdx for complex conditions. useClusters overrides cellIdx.

lambda

Numeric regularization parameter. By default NULL, this will use the lambda value used in the latest factorization.

nIteration

Maximum number of block coordinate descent iterations to perform. Default 30.

cellIdx

Valid index vector that applies to the whole object. See subsetLiger for requirement. Default NULL.

scaleDatasets

Names of datasets to re-scale after subsetting. Default NULL does not re-scale.

seed

Random seed to allow reproducible results. Default 1. Used by runINMF factorization.

verbose

Logical. Whether to show information of the progress. Default getOption("ligerVerbose") which is TRUE if users have not set.

cell.subset, cluster.subset, max.iters, datasets.scale

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.

Examples

pbmc <- normalize(pbmc)
pbmc <- selectGenes(pbmc)
pbmc <- scaleNotCenter(pbmc)
if (requireNamespace("RcppPlanc", quietly = TRUE)) {
    # Only running a few iterations for fast examples
    pbmc <- runINMF(pbmc, k = 20, nIteration = 2)
    pbmc <- optimizeSubset(pbmc, cellIdx = sort(sample(ncol(pbmc), 200)),
                           nIteration = 2)
}