cluster: 'FlowSOM' clustering & 'ConsensusClusterPlus' metaclustering

In HelenaLC/CATALYST: Cytometry dATa anALYSis Tools

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 features 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 specifying the grid size of the self-orginizing map; passed to BuildSOM. The default 10x10 grid will yield 100 clusters.
maxK	numeric specifying the maximum number of clusters to evaluate in the metaclustering; passed to ConsensusClusterPlus. The default (maxK = 20) will yield 2 through 20 metaclusters.
verbose	logical. Should information on progress be reported?
seed	numeric. Sets the random seed for reproducible results 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:

• colData

  • cluster_id: each cell's cluster ID as inferred by FlowSOM. One of 1, ..., xdimxydim.

• 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 details).

Author(s)

Helena L Crowell [e-mail]

References

Nowicka M, Krieg C, Crowell HL, 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 all available clustering
names(cluster_codes(sce))

# access specific clustering resolution
table(cluster_ids(sce, "meta8"))

# view delta area plot
delta_area(sce)

HelenaLC/CATALYST documentation built on Nov. 30, 2024, 4:04 a.m.