cp_clusterDPAEuclideanDist: Cluster using DPA
In protViz/prora: ORA, sigORA and GSEA functionalities for proteomic data
Description
Usage
Arguments
Value
See Also
Description
Cluster using DPA
Usage
1
cp_clusterDPAEuclideanDist(mdata, Z = 1, JK = TRUE)
Arguments
mdata,
input data
Z,
the number of standard deviations fixing the level of
statistical confidence at which one decides to consider
a cluster meaningful. Default value is set to 1.
JK
jack knive resampling (default TRUE)
Value
dendrogram - instance of class dendrogram
cluster assignments
nrCluster number of clusters
See Also
Other clusterProfiler:
cp_addurls_GO(),
cp_clusterHClustEuclideanDistDeepslit(),
cp_clusterHClustEuclideanDist(),
cp_compClust(),
cp_filterforNA()
protViz/prora documentation built on Dec. 12, 2021, 12:32 a.m.
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Description Usage Arguments Value See Also
Description
Cluster using DPA
Usage
1 | cp_clusterDPAEuclideanDist(mdata, Z = 1, JK = TRUE)
|
Arguments
mdata, |
input data |
Z, |
the number of standard deviations fixing the level of statistical confidence at which one decides to consider a cluster meaningful. Default value is set to 1. |
JK |
jack knive resampling (default TRUE) |
Value
dendrogram - instance of class dendrogram
cluster assignments
nrCluster number of clusters
See Also
Other clusterProfiler:
cp_addurls_GO(),
cp_clusterHClustEuclideanDistDeepslit(),
cp_clusterHClustEuclideanDist(),
cp_compClust(),
cp_filterforNA()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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