compareSpmCollection {KCsmart} | R Documentation

## KCsmart Comparative calculate null distribution

### Description

Compare the samples of one class in the sample point matrix collection to the samples in the other class and calculate the null distribution

### Usage

    compareSpmCollection(spmCollection, nperms=20, method=c("siggenes", "perm"), siggenes.args=NULL, altcl=NULL)

### Arguments

`spmCollection` | An spmCollection object as created by the 'calcSpmCollection' function
`nperms` | The number of permutations to be used to calculate the null distribution
`altcl` | Instead of using the class vector from the spmCollection object an alternative vector can be used
`method` | The method to be used to calculate the null distribution
`siggenes.args` | Optional additional arguments to the siggenes function

### Details

The method to be used to determine significant regions can either be the SAM methodology from the siggenes package or a signal-to-noise/permutation based method. For more information regarding the siggenes method please check the corresponding package.

### Value

Returns a compKc object which returns the original data and, depending on the method used, the permuted data or the fdr-delta value combinations as calculated by the siggenes package.

### Author(s)

Jorma de Ronde

### See Also

`compareSpmCollection`, `getSigRegionsCompKC`

### Examples

    data(hsSampleData)
    data(hsMirrorLocs)

    spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
    spmcc1mb <- compareSpmCollection(spmc1mb, nperms=3)
    spmcc1mbSigRegions <- getSigRegionsCompKC(spmcc1mb)

    plot(spmcc1mb, sigRegions=spmcc1mbSigRegions)

[Package KCsmart version 2.3.2 Index]