kmeansDesign {recoup} | R Documentation

## Apply k-means clustering to profile data

### Description

This function performs k-means clustering on `recoup` generated profile matrices and stores the result as a factor in the design element. If no design is present, then one is created from the k-means result.

### Usage

        kmeansDesign(input, design = NULL, kmParams)

### Arguments

`input` | a list object created from `recoup` or partially processed by `recoup` or its `data` member. See the main input to `recoup` for further information.
`design` | See the respective argument in `recoup` for further information
`kmParams` | Contains parameters for k-means clustering on profiles. See the respective argument in `recoup` for further information.

### Value

The design data frame, either created from scratch or augmented by k-means clustering.

### Author(s)

Panagiotis Moulos

### Examples

    # Load some data
    data("recoup_test_data",package="recoup")

    # Calculate coverages
    test.tss <- recoup(
        test.input,
        design=NULL,
        region="tss",
        type="chipseq",
        genome=test.genome,
        flank=c(1000,1000),
        selector=NULL,
        plotParams=list(plot=FALSE,profile=TRUE,
            heatmap=TRUE,device="x11"),
        rc=0.5
    )

    # Re-design based on k-means
    kmParams=list(k=2,nstart=20,algorithm="MacQueen",iterMax=20,
        reference=NULL,seed=42)
    design <- kmeansDesign(test.tss$data,kmParams=kmParams)

[Package recoup version 1.14.0 Index]