mismatchKernel {kebabs} | R Documentation

## Mismatch Kernel

### Description

Create a mismatch kernel object and the kernel matrix

### Usage

    mismatchKernel(k = 3, m = 1, r = 1, normalized = TRUE, exact = TRUE,
      ignoreLower = TRUE, presence = FALSE)

    ## S4 method for signature 'MismatchKernel'
    getFeatureSpaceDimension(kernel, x)

### Arguments

`k` | length of the substrings also called kmers; this parameter defines the size of the feature space, i.e. the total number of features considered in this kernel is |A|^k, with |A| as the size of the alphabet (4 for DNA and RNA sequences and 21 for amino acid sequences). Default=3
`m` | number of maximal mismatch per kmer. The allowed value range is between 1 and k-1. The processing effort for this kernel is highly dependent on the value of m and only small values will allow efficient processing. Default=1
`r` | exponent which must be > 0 (see details section in spectrumKernel). Default=1
`normalized` | a kernel matrix or explicit representation generated with this kernel will be normalized(details see below). Default=TRUE
`exact` | use exact character set for the evaluation (details see below). Default=TRUE
`ignoreLower` | ignore lower case characters in the sequence. If the parameter is not set lower case characters are treated like uppercase. Default=TRUE
`presence` | if this parameter is set only the presence of a kmers will be considered, otherwise the number of occurances of the kmer is used. Default=FALSE
`kernel` | a sequence kernel object
`x` | one or multiple biological sequences in the form of a `DNAStringSet`, `RNAStringSet`, `AAStringSet` (or as `BioVector`)

### Details

Creation of kernel object
The function 'mismatchKernel' creates a kernel object for the mismatch kernel. This kernel object can then be used with a set of DNA-, RNA- or AA-sequences to generate a kernel matrix or an explicit representation for this kernel. For values different from 1 (=default value) parameter `r` leads to a transfomation of similarities by taking each element of the similarity matrix to the power of r. If `normalized=TRUE`, the feature vectors are scaled to the unit sphere before computing the similarity value for the kernel matrix. For two samples with the feature vectors `x` and `y` the similarity is computed as:

s=(x^T y)/(|x| |y|)

For an explicit representation generated with the feature map of a normalized kernel the rows are normalized by dividing them through their Euclidean norm. For parameter `exact=TRUE` the sequence characters are interpreted according to an exact character set. If the flag is not set ambigous characters from the IUPAC characterset are also evaluated. The annotation specific variant (for details see positionMetadata) and the position dependent variant (for details see annotationMetadata) are not available for this kernel.
Creation of kernel matrix
The kernel matrix is created with the function `getKernelMatrix` or via a direct call with the kernel object as shown in the examples below.

### Value

mismatchKernel: upon successful completion, the function returns a kernel object of class `MismatchKernel`.

of getDimFeatureSpace: dimension of the feature space as numeric value

### Author(s)

Johannes Palme <kebabs@bioinf.jku.at>

### References

http://www.bioinf.jku.at/software/kebabs
(Leslie, 2002) – C. Leslie, E. Eskin, J. Weston and W.S. Noble. Mismatch String Kernels for SVM Protein Classification.
J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics, 31(15):2574-2576, 2015. DOI: 10.1093/bioinformatics/btv176.

### See Also

`kernelParameters`, `getKernelMatrix`, `getExRep`, `spectrumKernel`, `gappyPairKernel`, `motifKernel`, `MismatchKernel`

### Examples

    ## instead of user provided sequences in XStringSet format
    ## for this example a set of DNA sequences is created
    ## RNA- or AA-sequences can be used as well with the mismatch kernel
    dnaseqs <- DNAStringSet(c("AGACTTAAGGGACCTGGTCACCACGCTCGGTGAGGGGGACGGGGTGT",
                              "ATAAAGGTTGCAGACATCATGTCCTTTTTGTCCCTAATTATTTCAGC",
                              "CAGGAATCAGCACAGGCAGGGGCACGGCATCCCAAGACATCTGGGCC",
                              "GGACATATACCCACCGTTACGTGTCATACAGGATAGTTCCACTGCCC",
                              "ATAAAGGTTGCAGACATCATGTCCTTTTTGTCCCTAATTATTTCAGC"))
    names(dnaseqs) <- paste("S", 1:length(dnaseqs), sep="")

    ## create the kernel object with one mismatch per kmer
    mm <- mismatchKernel(k=2, m=1, normalized=FALSE)
    ## show details of kernel object
    mm

    ## generate the kernel matrix with the kernel object
    km <- mm(dnaseqs)
    dim(km)
    km[1:5, 1:5]

    ## alternative way to generate the kernel matrix
    km <- getKernelMatrix(mm, dnaseqs)
    km[1:5,1:5]

    ## Not run:
    ## plot heatmap of the kernel matrix
    heatmap(km, symm=TRUE)

    ## End(Not run)

[Package kebabs version 1.21.0 Index]