cluster {CATALYST} | R Documentation |
cluster
will first group cells into xdim
xydim
clusters using FlowSOM, and subsequently perform metaclustering
with ConsensusClusterPlus into 2 through maxK
clusters.
In the returned daFrame
, those antigens used for clustering will be
labelled as 'type
' markers, and the remainder of antigens as
'state
' markers.
cluster(x, ...) ## S4 method for signature 'daFrame' cluster(x, cols_to_use, xdim = 10, ydim = 10, maxK = 20, verbose = TRUE, seed = 1)
x |
a |
... |
optional arguments. |
cols_to_use |
a character vector. Specifies which antigens to use for clustering. |
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).
The function will add information to the following slots
of the input daFrame
(and return it):
rowData
cluster_id
:
each cell's cluster ID as inferred by FlowSOM
.
One of 1, ..., xdim
xydim
.
colData
marker_class
:
"type"
or "state"
.
Specifies whether an antigen has been used for clustering
or not, respectively.
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 crowellh@student.ethz.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)
data(PBMC_fs, PBMC_panel, PBMC_md) re <- daFrame(PBMC_fs, PBMC_panel, PBMC_md) # specify antigens to use for clustering lineage <- c("CD3", "CD45", "CD4", "CD20", "CD33", "CD123", "CD14", "IgM", "HLA_DR", "CD7") (re <- cluster(re, cols_to_use=lineage))