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.