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