aggregateFeatures: aggregateFeatures

In plger/scDblFinder: scDblFinder

aggregateFeatures

Description

Aggregates similar features (rows).

Usage

aggregateFeatures(
  x,
  dims.use = seq(2L, 12L),
  k = 1000,
  num_init = 3,
  use.mbk = NULL,
  use.subset = 20000,
  minCount = 1L,
  norm.fn = TFIDF,
  twoPass = FALSE,
  ...
)

Arguments

x	A integer/numeric (sparse) matrix, or a 'SingleCellExperiment' including a 'counts' assay.
dims.use	The PCA dimensions to use for clustering rows.
k	The approximate number of meta-features desired
num_init	The number of initializations used for k-means clustering.
use.mbk	Logical; whether to use minibatch k-means (see mbkmeans). If NULL, the minibatch approach will be used if there are more than 30000 features.
use.subset	How many cells (columns) to use to cluster the features.
minCount	The minimum number of counts for a region to be included.
norm.fn	The normalization function to use on the un-clustered data (a function taking a count matrix as a single argument and returning a matrix of the same dimensions). TFIDF by default.
twoPass	Logical; whether to perform the procedure twice, so in the second round cells are aggregated based on the meta-features of the first round, before re-clustering the features. Ignored if the dataset has fewer than 'use.subset' cells.
...	Passed to mbkmeans. Can for instance be used to pass the 'BPPARAM' argument for multithreading.

Value

An aggregated version of 'x' (either an array or a 'SingleCellExperiment', depending on the input). If 'x' is a 'SingleCellExperiment', the feature clusters will also be stored in 'metadata(x)$featureGroups'

plger/scDblFinder documentation built on Oct. 19, 2024, 6:26 a.m.