# 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

1 | ```
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

1 2 3 4 5 6 7 8 | ```
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)
``` |