seuratFindClusters {singleCellTK} | R Documentation

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

### Description

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

### Usage

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

### Arguments

`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`.

### Value

Updated sce object which now contains the computed clusters

### Examples

    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)

[Package singleCellTK version 2.0.0 Index]