Description Usage Arguments Details See Also Examples

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

1 2 3 4 5 6 7 8 | ```
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 is`"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""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".

`ClusterFunction`

, `algorithmType`

, `inputType`

1 2 3 4 5 | ```
listBuiltInFunctions()
algorithmType(c("kmeans","pam","hierarchical01"))
inputType(c("kmeans","pam","hierarchical01"))
listBuiltInTypeK()
listBuiltInType01()
``` |

Bioconductor-mirror/clusterExperiment documentation built on Aug. 2, 2017, 4:28 p.m.

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