Perform SNN graph clustering on a
SingleCellExperiment
object, with graph
construction by buildSNNGraph
and graph clustering by
"igraph" package.
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")
)
A SingleCellExperiment
object.
A single character
, specifying which
assay
to perform the clustering algorithm
on. Default NULL
.
A single character
, specifying which
low-dimension representation (reducedDim
)
to perform the clustering algorithm on. Default NULL
.
A single character
, specifying the assay which
altExp
to perform the clustering
algorithm on. Default NULL
.
A single character
, specifying which
assay
in the chosen
altExp
to work on. Only used when
useAltExp
is set. Default "counts"
.
A single character
, specifying which
reducedDim
within the altExp
specified by
useAltExp
to use. Only used when useAltExp
is set. Default
NULL
.
A single character
, specifying the name to store
the cluster label in colData
. Default
"scranSNN_cluster"
.
An integer
, the number of nearest neighbors used to construct
the graph. Smaller value indicates higher resolution and larger number of
clusters. Default 10
.
An integer
, the number of components to use when
useAssay
or useAltExp
is specified. WON'T work with
useReducedDim
. Default 50
.
A single character
, that specifies the edge weighing
scheme when constructing the Shared Nearest-Neighbor (SNN) graph. Choose from
"rank"
, "number"
, "jaccard"
. Default "rank"
.
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"
.
The input SingleCellExperiment
object with factor
cluster labeling updated in
colData(inSCE)[[clusterName]]
.
Aaron Lun and et. al., 2016
data("mouseBrainSubsetSCE")
mouseBrainSubsetSCE <- runScranSNN(mouseBrainSubsetSCE,
useReducedDim = "PCA_logcounts")
#> Thu Apr 28 11:28:51 2022 ... Running 'scran SNN clustering'