cluster {CATALYST}R Documentation

FlowSOM clustering & ConsensusClusterPlus metaclustering

Description

cluster will first group cells into xdimxydim clusters using FlowSOM, and subsequently perform metaclustering with ConsensusClusterPlus into 2 through maxK clusters.

Usage

cluster(
  x,
  features = "type",
  xdim = 10,
  ydim = 10,
  maxK = 20,
  verbose = TRUE,
  seed = 1
)

Arguments

x

a SingleCellExperiment.

features

a character vector specifying which antigens to use for clustering; valid values are "type"/"state" for type/state_markers(x) if rowData(x)$marker_class have been specified; a subset of rownames(x); NULL to use all features.

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 maxK = 20, for example, metaclustering will be performed for 2 through 20 clusters.

verbose

logical. Should information on progress be reported?

seed

numeric. Sets random seed in ConsensusClusterPlus().

Details

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).

Value

a SingleCellEcperiment with the following newly added data:

Author(s)

Helena Lucia Crowell helena.crowell@uzh.ch

References

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)

Examples

# 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)


[Package CATALYST version 1.10.3 Index]