cluster will first group cells into
clusters using FlowSOM, and subsequently perform metaclustering
with ConsensusClusterPlus into 2 through
1 2 3 4 5 6 7 8 9
a character vector specifying
which features to use for clustering; valid values are
numeric specifying the grid size of the
self-orginizing map; passed to
numeric specifying the maximum number of
clusters to evaluate in the metaclustering; passed to
logical. Should information on progress be reported?
numeric. Sets the random seed for reproducible results
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).
SingleCellEcperiment with the following newly added data:
each cell's cluster ID as inferred by
One of 1, ...,
marker_class: added when previosly unspecified.
when an antigen has been used for clustering, otherwise
used_for_clustering: logical indicating
whether an antigen has been used for clustering.
a table with dimensions K x (# cell type markers),
where K =
ydim. Contains the SOM codes.
a table with dimensions K x (
maxK + 1).
Contains the cluster codes for all metaclustering.
ggplot object (see details).
Helena L Crowell firstname.lastname@example.org
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)
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# 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)
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