builtInClusteringFunctions: Built in ClusterFunction options
In clusterExperiment: Compare Clusterings for Single-Cell Sequencing

Description Usage Arguments Details Value See Also Examples

Documents the built-in clustering options that are available in the clusterExperiment package.

listBuiltInFunctions()

## S4 method for signature 'character'
getBuiltInFunction(object)

listBuiltInTypeK()

listBuiltInType01()

object

name of built in function.

listBuiltInFunctions will return the character names of the built-in clustering functions available.

listBuiltInTypeK returns the names of the built-in functions that have type 'K'

listBuiltInType01 returns the names of the built-in functions that have type '01'

getBuiltInFunction will return the ClusterFunction object of a character value that corresponds to a built-in function.

algorithmType and inputType will return the algorithmType and inputType of the built-in clusterFunction corresponding to the character value.

Built-in clustering methods: The built-in clustering methods, the names of which can be accessed by listBuiltInFunctions() are the following:

"pam"Based on pam in cluster package. Arguments to that function can be passed via clusterArgs. Input can be either "x" or "diss"; algorithm type is "K"
"clara"Based on clara in cluster package. Arguments to that function can be passed via clusterArgs. Note that we have changed the default arguments of that function to match the recommendations in the documentation of clara (numerous functions are set to less than optimal settings for back-compatiability). Specifically, the following defaults are implemented samples=50, keep.data=FALSE, mediods.x=FALSE,rngR=TRUE, pamLike=TRUE, correct.d=TRUE. Input is "X"; algorithm type is "K".
"kmeans"Based on kmeans in stats package. Arguments to that function can be passed via clusterArgs except for centers which is reencoded here to be the argument 'k' Input is "X"; algorithm type is "K"
"mbkmeans"mbkmeans runs mini-batch kmeans, a more computationally efficient version of kmeans.
"hierarchical01"hclust in stats package is used to build hiearchical clustering. Arguments to that function can be passed via clusterArgs. The hierarchical01 cuts the hiearchical tree based on the parameter alpha. It does not use the cutree function, but instead transversing down the tree until getting a block of samples with whose summary of the values is greater than or equal to 1-alpha. Arguments that can be passed to 'hierarchical01' are 'evalClusterMethod' which determines how to summarize the samples' values of D[samples,samples] for comparison to 1-alpha: "maximum" (default) takes the minimum of D[samples,samples] and requires it to be less than or equal to 1-alpha; "average" requires that each row mean of D[samples,samples] be less than or equal to 1-alpha. Additional arguments of hclust can also be passed via clusterArgs to control the hierarchical clustering of D. Input is "diss"; algorithm type is "01"
"hierarchicalK"hclust in stats package is used to build hiearchical clustering and cutree is used to cut the tree into k clusters. Input is "diss"; algorithm type is "K"
"tight"Based on the algorithm in Tsang and Wong, specifically their method of picking clusters from a co-occurance matrix after subsampling. The clustering encoded here is not the entire tight clustering algorithm, only that single piece that identifies clusters from the co-occurance matrix. Arguments for the tight method are 'minSize.core' (default=2), which sets the minimimum number of samples that form a core cluster. Input is "diss"; algorithm type is "01"
"spectral"specc in kernlab package is used to perform spectral clustering. Note that spectral clustering can produce errors if the number of clusters (K) is not sufficiently smaller than the number of samples (N). K < N is not always sufficient. Input is "X"; algorithm type is "K".

listBuiltInFunctions returns a character vector of all the built-in cluster functions' names.

getBuiltInFunction returns the ClusterFunction object that corresponds to the character name of a function

listBuiltInTypeK returns a character vector of the names of built-in cluster functions that are of type "K"

listBuiltInType01 returns a character vector of the names of built-in cluster functions that are of type "01"

ClusterFunction, algorithmType, inputType

listBuiltInFunctions()
algorithmType(c("kmeans","pam","hierarchical01"))
inputType(c("kmeans","pam","hierarchical01"))
listBuiltInTypeK()
listBuiltInType01()