seuratFindClusters: seuratFindClusters Computes the clusters from the input sce...
In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description Usage Arguments Value Examples

seuratFindClusters Computes the clusters from the input sce object and stores them back in sce object

seuratFindClusters(
  inSCE,
  useAssay,
  useReduction = c("pca", "ica"),
  dims = 10,
  algorithm = c("louvain", "multilevel", "SLM"),
  groupSingletons = TRUE,
  resolution = 0.8,
  externalReduction = NULL
)

`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 `"pca"`.
`dims`	numeric value of how many components to use for computing clusters. Default `10`.
`algorithm`	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`.
`groupSingletons`	boolean if singletons should be grouped together or not. Default `TRUE`.
`resolution`	Set the resolution parameter to find larger (value above 1) or smaller (value below 1) number of communities. Default `0.8`.
`externalReduction`	Pass DimReduc object if PCA/ICA computed through other libraries. Default `NULL`.

Updated sce object which now contains the computed clusters

data(scExample, package = "singleCellTK")
## Not run: 
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")

## End(Not run)