internalFunctionCheck {clusterExperiment} | R Documentation

## Class ClusterFunction

### Description

`ClusterFunction` is a class for holding functions that can be used for clustering in the clustering algorithms in this package.

The constructor `ClusterFunction` creates an object of the class `ClusterFunction`.

### Usage

    internalFunctionCheck(clusterFUN, inputType, algorithmType, outputType)

    ClusterFunction(clusterFUN, ...)

    ## S4 method for signature ''function''
    ClusterFunction(
      clusterFUN,
      inputType,
      outputType,
      algorithmType,
      inputClassifyType = NA_character_,
      requiredArgs = NA_character_,
      classifyFUN = NULL,
      checkFunctions = TRUE
    )

### Arguments

`clusterFUN` | function passed to slot `clusterFUN`.
`inputType` | character for slot `inputType`
`algorithmType` | character for slot `inputType`
`outputType` | character for slot `outputType`
`...` | arguments passed to different methods of `ClusterFunction`
`inputClassifyType` | character for slot `inputClassifyType`
`requiredArgs` | character for slot `requiredArgs`
`classifyFUN` | function for slot `classifyFUN`
`checkFunctions` | logical for whether to check the input functions with `internalFunctionsCheck`

### Details

`internalFunctionCheck` is the function that is called by the validity check of the `ClusterFunction` constructor (if `checkFunctions=TRUE`). It is available as an S3 function for the user to be able to test their functions and debug them, which is difficult to do with a S4 validity function.

clusterFUN: The following arguments are required to be accepted for `clusterFUN` – higher-level code may pass these arguments (but the function can ignore them or just have be handled with a ... )

- "inputMatrix"will be the matrix of data

- "inputType"one of "X", "diss", or "cat". If "X", then `inputMatrix` is assumed to be nfeatures x nsamples (like assay(CEObj) would give). If "cat" then nfeatures x nsamples, but all entries should be categorical levels, encoded by positive integers, with -1/-2 types of NA (like a clusterMatrix slot, but with dimensions switched). If "diss", then `inputMatrix` should be a nxn dissimilarity matrix.

- "checkArgs"logical argument. If `checkArgs=TRUE`, the `clusterFUN` should check if the arguments passed in `...` are valid and return an error if not; otherwise, no error will be given, but the check should be done and only valid arguments in `...` passed along. This is necessary for the function to work with `clusterMany` which passes all arguments to all functions without checking.

- "cluster.only"logical argument. If `cluster.only=TRUE`, then `clusterFUN` should return only the vector of cluster assignments (or list if `outputType="list"`). If `cluster.only=FALSE` then the `clusterFUN` should return a named list where one of the elements entitled `clustering` contains the vector described above (no list allowed!); anything else needed by the `classifyFUN` to classify new data should be contained in the output list as well. `cluster.only` is set internally depending on whether `classifyFUN` will be later used by subsampling or only for clustering the final product.

- "..."Any additional arguments specific to the algorithm used by `clusterFUN` should be passed via `...` and NOT passed via arguments to `clusterFUN`

- "Other required arguments"`clusterFUN` must also accept arguments required for its `algorithmType` (see Details below).

classifyFUN: The following arguments are required to be accepted for `classifyFUN` (if not NULL)

- inputMatrixthe new data that will be classified into the clusters

- inputTypethe inputType of the new data (see above)

- clusterResultthe result of running `clusterFUN` on the training data, when `cluster.only=FALSE`. Whatever is returned by `clusterFUN` is assumed to be sufficient for this function to classify new objects (e.g. could return the centroids of the clustering, if clustering based on nearest centroid).

algorithmType: Type "01" is for clustering functions that expect as an input a dissimilarity matrix that takes on 0-1 values (e.g. from subclustering) with 1 indicating more dissimilarity between samples. "01" algorithm types must also have `inputType` equal to `"diss"`. It is also generally expected that "01" algorithms use the 0-1 nature of the input to set criteria as to where to find clusters. "01" functions must take as an argument `alpha` between 0 and 1 to determine the clusters, where larger values of `alpha` require less similarity between samples in the same cluster. "K" is for clustering functions that require an argument `k` (the number of clusters), but arbitrary `inputType`. On the other hand, "K" algorithms are assumed to need a predetermined 'k' and are also assumed to cluster all samples to a cluster. If not, the post-processing steps in `mainClustering` such as `findBestK` and `removeSil` may not operate correctly since they rely on silhouette distances.

### Value

Returns a logical value of TRUE if there are no problems. If there is a problem, returns a character string describing the problem encountered.

A `ClusterFunction` object.

### Slots

`clusterFUN`

  a function defining the clustering function. See details for required arguments.

`inputType`

  a character vector defining what type(s) of input `clusterFUN` takes. Must consist of values "diss","X", or "cat" indicating the set of input values that the algorithm can handle (see details below).

`algorithmType`

  a character defining what type of clustering algorithm `clusterFUN` is. Must be one of either "01" or "K". `clusterFUN` must take the corresponding required arguments for its type (see details below).

`classifyFUN`

  a function that has takes as input new data and the output of `clusterFUN` (where the output is from when `cluster.only=FALSE`) and results in cluster assignments of the new data. Used in subsampling clustering. Note that the function should assume that the data given to the `inputMatrix` argument is not the same samples that were input to the ClusterFunction (but does assume that it is the same number of features/columns). If slot `classifyFUN` is given value `NULL` then subsampling type can only be `"InSample"`, see `subsampleClustering`.

`inputClassifyType`

  the input type for the classification function (if not NULL); like `inputType`, must be a vector containing "diss","X", or "cat"

`outputType`

  the type of output given by `clusterFUN`. Must either be "vector" or "list". If "vector" then the output should be a vector of length equal to the number of observations with integer-valued elements identifying them to different clusters; the vector assignments should be in the same order as the original input of the data. Samples that are not assigned to any cluster should be given a '-1' value. If "list", then it must be a list equal to the length of the number of clusters, and the elements of the list contain the indices of the samples in that cluster. Any indices not in any of the list elements are assumed to be -1. The main advantage of "list" is that it can preserve the order of the clusters if the `clusterFUN` desires to do so. In which case the `orderBy` argument of `mainClustering` can preserve this ordering (default is to order by size).

`requiredArgs`

  Any additional required arguments for `clusterFUN` (beyond those required of all `clusterFUN`, described in details). Will be used in checking that user provided necessary arguments.

`checkFunctions`

  logical. If TRUE, the validity check of the `ClusterFunction` object will check the `clusterFUN` with simple toy data using the function `internalFunctionCheck`.

### Examples

    #Use internalFunctionCheck to check possible function
    goodFUN<-function(inputMatrix,k,cluster.only,...){
    cluster::pam(x=t(inputMatrix),k=k,cluster.only=cluster.only)
    }
    #passes internal check
    internalFunctionCheck(goodFUN,inputType=c("X","diss"),
       algorithmType="K",outputType="vector")
    myCF<-ClusterFunction(clusterFUN=goodFUN, inputType="X",
       algorithmType="K", outputType="vector")
    #doesn't work, because haven't made results return vector when cluster.only=TRUE
    badFUN<-function(inputMatrix,k,cluster.only,...){cluster::pam(x=inputMatrix,k=k)}
    internalFunctionCheck(badFUN,inputType=c("X","diss"),
       algorithmType="K",outputType="vector")

[Package clusterExperiment version 2.10.1 Index]