NNGraphParam-class: Graph-based clustering
In LTLA/bluster: Clustering Algorithms for Bioconductor

NNGraphParam-class

R Documentation

Graph-based clustering

Description

Run community detection algorithms on a nearest-neighbor (NN) graph within clusterRows.

Usage

NNGraphParam(
  shared = TRUE,
  k = 10,
  ...,
  BNPARAM = KmknnParam(),
  BPPARAM = SerialParam(),
  cluster.fun = "walktrap",
  cluster.args = list()
)

SNNGraphParam(
  k = 10,
  type = "rank",
  BNPARAM = KmknnParam(),
  BPPARAM = SerialParam(),
  cluster.fun = "walktrap",
  cluster.args = list()
)

KNNGraphParam(
  k = 10,
  directed = FALSE,
  BNPARAM = KmknnParam(),
  BPPARAM = SerialParam(),
  cluster.fun = "walktrap",
  cluster.args = list()
)

## S4 method for signature 'ANY,SNNGraphParam'
clusterRows(x, BLUSPARAM, full = FALSE)

## S4 method for signature 'ANY,KNNGraphParam'
clusterRows(x, BLUSPARAM, full = FALSE)

Arguments

`shared`	Logical scalar indicating whether a shared NN graph should be constructed.
`k`	An integer scalar specifying the number of nearest neighbors to consider during graph construction.
`...`	Further arguments to pass to `SNNGraphParam` (if `shared=TRUE`) or `KNNGraphParam`.
`BNPARAM`	A BiocNeighborParam object specifying the nearest neighbor algorithm.
`BPPARAM`	A BiocParallelParam object to use for parallel processing.
`cluster.fun`	Function specifying the method to use to detect communities in the NN graph. The first argument of this function should be the NN graph and the return value should be a communities object. Alternatively, this may be a string containing the suffix of any igraph community detection algorithm. For example, `cluster.fun="louvain"` will instruct `clusterRows` to use `cluster_louvain`. Defaults to `cluster_walktrap`.
`cluster.args`	Further arguments to pass to the chosen `cluster.fun`.
`type`	A string specifying the type of weighting scheme to use for shared neighbors.
`directed`	A logical scalar indicating whether the output of `buildKNNGraph` should be a directed graph.
`x`	A matrix-like object containing expression values for each observation (row) and dimension (column).
`BLUSPARAM`	A NNGraphParam object.
`full`	Logical scalar indicating whether the graph-based clustering objects should be returned.

Details

The SNNGraphParam and KNNGraphParam classes are both derived from the NNGraphParam virtual class. This former will perform clustering with a shared nearest-neighbor (SNN) graph while the latter will use a simpler k-nearest neighbor (KNN) graph - see ?makeSNNGraph for details.

To modify an existing NNGraphParam object x, users can simply call x[[i]] or x[[i]] <- value where i is any argument used in the constructor. The exception is that of shared, which is not a valid i as it is implicit in the identity of the class.

Value

The constructors will return a NNGraphParam object with the specified parameters. If shared=TRUE, this is a SNNGraphParam object; otherwise it is a KNNGraphParam object.

The clusterRows method will return a factor of length equal to nrow(x) containing the cluster assignments. If full=TRUE, a list is returned with clusters (the factor, as above) and objects; the latter is a list with graph (the graph) and communities (the output of cluster.fun).

Author(s)

Aaron Lun

Examples

clusterRows(iris[,1:4], NNGraphParam())
clusterRows(iris[,1:4], NNGraphParam(k=5))

# Note: cluster_louvain is randomized as of igraph 1.3.0.
set.seed(100)
clusterRows(iris[,1:4], NNGraphParam(cluster.fun="louvain"))

# On the plus side, we can finally pass a resolution parameter.
set.seed(100)
clusterRows(iris[,1:4], NNGraphParam(cluster.fun="louvain", 
    cluster.args=list(resolution=0.5)))

LTLA/bluster documentation built on Aug. 20, 2023, 5:39 a.m.