clusterSNNGraph: Wrappers for graph-based clustering
In scran: Methods for Single-Cell RNA-Seq Data Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

Perform graph-based clustering using community detection methods on a nearest-neighbor graph, where nodes represent cells or k-means centroids. This has been deprecated in favor of directly using clusterRows from the bluster package, with BLUSPARAM set to NNGraphParam() or TwoStepParam().

clusterSNNGraph(x, ...)

## S4 method for signature 'ANY'
clusterSNNGraph(
  x,
  ...,
  clusterFUN = cluster_walktrap,
  subset.row = NULL,
  transposed = FALSE,
  use.kmeans = FALSE,
  kmeans.centers = NULL,
  kmeans.args = list(),
  full.stats = FALSE
)

## S4 method for signature 'SummarizedExperiment'
clusterSNNGraph(x, ..., assay.type = "logcounts")

## S4 method for signature 'SingleCellExperiment'
clusterSNNGraph(x, ..., use.dimred = NULL)

clusterKNNGraph(x, ...)

## S4 method for signature 'ANY'
clusterKNNGraph(
  x,
  ...,
  clusterFUN = cluster_walktrap,
  subset.row = NULL,
  transposed = FALSE,
  use.kmeans = FALSE,
  kmeans.centers = NULL,
  kmeans.args = list(),
  full.stats = FALSE
)

## S4 method for signature 'SummarizedExperiment'
clusterKNNGraph(x, ..., assay.type = "logcounts")

## S4 method for signature 'SingleCellExperiment'
clusterKNNGraph(x, ..., use.dimred = NULL)

`x`	A matrix-like object containing expression values for each gene (row) in each cell (column). These dimensions can be transposed if `transposed=TRUE`. Alternatively, a SummarizedExperiment or SingleCellExperiment containing such an expression matrix. If `x` is a SingleCellExperiment and `use.dimred` is set, its `reducedDims` will be used instead.
`...`	For the generics, additional arguments to pass to the specific methods. For the ANY methods, additional arguments to pass to `buildSNNGraph` or `buildKNNGraph`. For the SummarizedExperiment method, additional arguments to pass to the corresponding ANY method. For the SingleCellExperiment method, additional arguments to pass to the corresponding SummarizedExperiment method.
`clusterFUN`	Function that can take a graph object and return a communities object, see examples in the igraph package.
`subset.row`	See `?"scran-gene-selection"`. Only used when `transposed=FALSE`.
`transposed`	A logical scalar indicating whether `x` is transposed (i.e., rows are cells).
`use.kmeans`	Logical scalar indicating whether k-means clustering should be performed.
`kmeans.centers`	Integer scalar specifying the number of clusters to use for k-means clustering. Defaults to the square root of the number of cells in `x`.
`kmeans.args`	List containing additional named arguments to pass to `kmeans`.
`full.stats`	Logical scalar indicating whether to return more statistics regarding the k-means clustering.
`assay.type`	A string specifying which assay values to use.
`use.dimred`	A string specifying whether existing values in `reducedDims(x)` should be used.

We suggest using the clusterRows functionality instead as it provides a more general interface to clustering.

clusterSNNGraph(x) for a matrix-like object x is equivalent to clusterRows(t(x), NNGraphParam()).
clusterKNNGraph(x) for a matrix-like object x is equivalent to clusterRows(t(x), NNGraphParam(shared=FALSE)).
clusterSNNGraph(x) for a SummarizedExperiment object x is equivalent to clusterRows(t(logcounts(x)), NNGraphParam()).
clusterSNNGraph(x, use.dimred="PCA") for a SingleCellExperiment object x is equivalent to clusterRows(reducedDim(x, "PCA"), NNGraphParam()).
clusterSNNGraph(x, use.kmeans=TRUE, use.dimred="PCA") for a SingleCellExperiment object x is equivalent to clusterRows(reducedDim(x, "PCA"), TwoStepParam()).

If full.stats=FALSE, a factor is returned containing the cluster assignment for each cell.

If full.stats=TRUE and use.kmeans=TRUE, a DataFrame is returned with one row per cell. This contains the columns kmeans, specifying the assignment of each cell to a k-means cluster; and igraph, specifying the assignment of each cell to a graph-based cluster operating on the k-means clusters. In addition, the metadata contains graph, a graph object where each node is a k-means cluster; and membership, the graph-based cluster to which each node is assigned.

Aaron Lun

clusterRows with BLUSPARAM set to an instance of NNGraphParam or TwoStepParam.

library(scuttle)
sce <- mockSCE(ncells=500)
sce <- logNormCounts(sce)

clusters <- clusterSNNGraph(sce)
table(clusters)

# Can pass usual arguments to buildSNNGraph:
clusters2 <- clusterSNNGraph(sce, k=5)
table(clusters2)

# Works with low-dimensional inputs:
sce <- scater::runPCA(sce, ncomponents=10)
clusters3 <- clusterSNNGraph(sce, use.dimred="PCA")
table(clusters3)

# Turn on k-means for larger datasets, e.g., 
# assuming we already have a PCA result:
set.seed(101010)
bigpc <- matrix(rnorm(2000000), ncol=20)
clusters4 <- clusterSNNGraph(bigpc, d=NA, use.kmeans=TRUE, transposed=TRUE)
table(clusters4)

# Extract the graph for more details:
clusters5 <- clusterSNNGraph(sce, use.dimred="PCA", 
    use.kmeans=TRUE, full.stats=TRUE)
head(clusters5)
metadata(clusters5)$graph