R/seuratFunctions.R
runSeuratFindClusters.Rd
runSeuratFindClusters Computes the clusters from the input sce object and stores them back in sce object
(sce) object from which clusters should be computed and stored in
Assay containing scaled counts to use for clustering.
Reduction method to use for computing clusters. One of
"pca" or "ica". Default "pca"
.
numeric value of how many components to use for computing
clusters. Default 10
.
selected algorithm to compute clusters. One of "louvain",
"multilevel", or "SLM". Use louvain
for "original Louvain algorithm"
and multilevel
for "Louvain algorithm with multilevel refinement".
Default louvain
.
boolean if singletons should be grouped together or
not. Default TRUE
.
Set the resolution parameter to find larger (value above 1)
or smaller (value below 1) number of communities. Default 0.8
.
Pass DimReduc object if PCA/ICA computed through
other libraries. Default NULL
.
Logical value indicating if informative messages should
be displayed. Default is TRUE
.
Updated sce object which now contains the computed clusters
data(scExample, package = "singleCellTK")
if (FALSE) {
sce <- runSeuratNormalizeData(sce, useAssay = "counts")
sce <- runSeuratFindHVG(sce, useAssay = "counts")
sce <- runSeuratScaleData(sce, useAssay = "counts")
sce <- runSeuratPCA(sce, useAssay = "counts")
sce <- runSeuratFindClusters(sce, useAssay = "counts")
}