cluster {CATALYST} | R Documentation |
FlowSOM
clustering &
ConsensusClusterPlus
metaclusteringcluster
will first group cells into xdim
xydim
clusters using FlowSOM, and subsequently perform metaclustering
with ConsensusClusterPlus into 2 through maxK
clusters.
cluster( x, features = "type", xdim = 10, ydim = 10, maxK = 20, verbose = TRUE, seed = 1 )
x |
|
features |
a character vector specifying
which antigens to use for clustering; valid values are
|
xdim, ydim |
numeric. Specify the grid size of the self-orginizing map. The default 10x10 grid will yield 100 clusters. |
maxK |
numeric. Specifies the maximum number of clusters to evaluate
in the metaclustering. For |
verbose |
logical. Should information on progress be reported? |
seed |
numeric. Sets random seed in |
The delta area represents the amount of extra cluster stability gained when clustering into k groups as compared to k-1 groups. It can be expected that high stability of clusters can be reached when clustering into the number of groups that best fits the data. The "natural" number of clusters present in the data should thus corresponds to the value of k where there is no longer a considerable increase in stability (pleateau onset).
a SingleCellEcperiment
with the following newly added data:
colData
cluster_id
:
each cell's cluster ID as inferred by FlowSOM
.
One of 1, ..., xdim
xydim
.
rowData
marker_class
: added when previosly unspecified. "type"
when an antigen has been used for clustering, otherwise "state"
.
used_for_clustering
: logical indicating
whether an antigen has been used for clustering.
metadata
SOM_codes
:
a table with dimensions K x (# cell type markers),
where K = xdim
x ydim
. Contains the SOM codes.
cluster_codes
:
a table with dimensions K x (maxK
+ 1).
Contains the cluster codes for all metaclustering.
delta_area
:
a ggplot
object. See above for details.
Helena Lucia Crowell helena.crowell@uzh.ch
Nowicka M, Krieg C, Weber LM et al. CyTOF workflow: Differential discovery in high-throughput high-dimensional cytometry datasets. F1000Research 2017, 6:748 (doi: 10.12688/f1000research.11622.1)
# construct SCE data(PBMC_fs, PBMC_panel, PBMC_md) sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md) # run clustering (sce <- cluster(sce)) #' # view delta area plot library(SingleCellExperiment) metadata(sce)$delta_area # exract cluster IDs for a specific resolution cluster_ids_meta8 <- cluster_ids(sce, k = "meta8") table(cluster_ids_meta8)