R: Get clustering with SNN graph

runScranSNN {singleCellTK}

R Documentation

Get clustering with SNN graph

Description

Perform SNN graph clustering on a SingleCellExperiment object, with graph construction by buildSNNGraph and graph clustering by "igraph" package.

Usage

runScranSNN(
  inSCE,
  useAssay = NULL,
  useReducedDim = NULL,
  useAltExp = NULL,
  altExpAssay = "counts",
  altExpRedDim = NULL,
  clusterName = "scranSNN_cluster",
  k = 10,
  nComp = 50,
  weightType = c("rank", "number", "jaccard"),
  algorithm = c("walktrap", "louvain", "infomap", "fastGreedy", "labelProp",
    "leadingEigen")
)

Arguments

`inSCE`	A `SingleCellExperiment` object.
`useAssay`	A single `character`, specifying which `assay` to perform the clustering algorithm on. Default `NULL`.
`useReducedDim`	A single `character`, specifying which low-dimension representation (`reducedDim`) to perform the clustering algorithm on. Default `NULL`.
`useAltExp`	A single `character`, specifying the assay which `altExp` to perform the clustering algorithm on. Default `NULL`.
`altExpAssay`	A single `character`, specifying which `assay` in the chosen `altExp` to work on. Only used when `useAltExp` is set. Default `"counts"`.
`altExpRedDim`	A single `character`, specifying which `reducedDim` within the `altExp` specified by `useAltExp` to use. Only used when `useAltExp` is set. Default `NULL`.
`clusterName`	A single `character`, specifying the name to store the cluster label in `colData`. Default `"scranSNN_cluster"`.
`k`	An `integer`, the number of nearest neighbors used to construct the graph. Smaller value indicates higher resolution and larger number of clusters. Default `10`.
`nComp`	An `integer`, the number of components to use when `useAssay` or `useAltExp` is specified. WON'T work with `useReducedDim`. Default `50`.
`weightType`	A single `character`, that specifies the edge weighing scheme when constructing the Shared Nearest-Neighbor (SNN) graph. Choose from `"rank"`, `"number"`, `"jaccard"`. Default `"rank"`.
`algorithm`	A single `character`, that specifies the community detection algorithm to work on the SNN graph. Choose from `"walktrap"`, `"louvain"`, `"infomap"`, `"fastGreedy"`, `"labelProp"`, `"leadingEigen"`. Default `"walktrap"`.

Value

The input SingleCellExperiment object with factor cluster labeling updated in colData(inSCE)[[clusterName]].

References

Aaron Lun and et. al., 2016

Examples

data("mouseBrainSubsetSCE")
mouseBrainSubsetSCE <- runScranSNN(mouseBrainSubsetSCE,
                                   useReducedDim = "PCA_logcounts")

[Package singleCellTK version 2.4.0 Index]