clusKNN: A wrapper function for 'Seurat"s 'FindNeighbors' and...
In scDataviz: scDataviz: single cell dataviz and downstream analyses

Description Usage Arguments Details Value Author(s) Examples

A wrapper function for Seurat's FindNeighbors and FindClusters.

clusKNN(
  indata,
  reducedDim = "UMAP",
  dimColnames = c("UMAP1", "UMAP2"),
  clusterAssignName = "Cluster",
  distance.matrix = FALSE,
  k.param = 20,
  compute.SNN = TRUE,
  prune.SNN = 1/15,
  nn.method = "rann",
  annoy.metric = "euclidean",
  nn.eps = 0,
  verbose = TRUE,
  force.recalc = FALSE,
  modularity.fxn = 1,
  initial.membership = NULL,
  weights = NULL,
  node.sizes = NULL,
  resolution = 0.8,
  method = "matrix",
  algorithm = 1,
  n.start = 10,
  n.iter = 10,
  random.seed = 0,
  group.singletons = TRUE,
  temp.file.location = NULL,
  edge.file.name = NULL,
  overwrite = FALSE
)

`indata`	A data-frame or matrix, or `SingleCellExperiment` object. If a `SingleCellExperiment` object, the cell-to-cluster assignments will be added as a new column, specified by `clusterAssignName`, to the input object's metadata; if a data-frame or matrix, only the cluster assignment vector is returned.
`reducedDim`	A reduced dimensional component stored within `indata`. e.g., PCA or UMAP.
`dimColnames`	The column names of the dimensions to use.
`clusterAssignName`	The new column name in the metadata that will contain the determined cell-to-cluster assignments.
`distance.matrix`	Refer to `?Seurat::FindNeighbors`.
`k.param`	Refer to `?Seurat::FindNeighbors`.
`compute.SNN`	Refer to `?Seurat::FindNeighbors`.
`prune.SNN`	Refer to `?Seurat::FindNeighbors`.
`nn.method`	Refer to `?Seurat::FindNeighbors`.
`annoy.metric`	Refer to `?Seurat::FindNeighbors`.
`nn.eps`	Refer to `?Seurat::FindNeighbors`.
`verbose`	Refer to `?Seurat::FindNeighbors`.
`force.recalc`	Refer to `?Seurat::FindNeighbors`.
`modularity.fxn`	Refer to `?Seurat::FindClusters`.
`initial.membership`	Refer to `?Seurat::FindClusters`.
`weights`	Refer to `?Seurat::FindClusters`.
`node.sizes`	Refer to `?Seurat::FindClusters`.
`resolution`	Refer to `?Seurat::FindClusters`.
`method`	Refer to `?Seurat::FindClusters`.
`algorithm`	Refer to `?Seurat::FindClusters`.
`n.start`	Refer to `?Seurat::FindClusters`.
`n.iter`	Refer to `?Seurat::FindClusters`.
`random.seed`	Refer to `?Seurat::FindClusters`.
`group.singletons`	Refer to `?Seurat::FindClusters`.
`temp.file.location`	Refer to `?Seurat::FindClusters`.
`edge.file.name`	Refer to `?Seurat::FindClusters`.
`overwrite`	When the input object is a SingleCellExperiment, enabling this will result in the overwriting, with the new cluster assignments, of any column in your metadata that has the same name as `clusterAssignName`.

A wrapper function for Seurat's FindNeighbors and FindClusters.

A SingleCellExperiment or numeric object.

Kevin Blighe <kevin@clinicalbioinformatics.co.uk>

# create random data that follows a negative binomial
mat <- jitter(matrix(
  MASS::rnegbin(rexp(1000, rate=.1), theta = 4.5),
  ncol = 20))
colnames(mat) <- paste0('CD', 1:ncol(mat))
rownames(mat) <- paste0('cell', 1:nrow(mat))

clusKNN(mat)