Description Usage Arguments Details Value Author(s) References See Also Examples
Create a gappy pair kernel object and the kernel matrix
1 2 3 4 5 6 7 
k 
length of the substrings (also called kmers) which are considered in pairs by this kernel. This parameter together with parameter m (see below) defines the size of the feature space, i.e. the total number of features considered in this kernel is (A^(2*k))*(m+1), with A as the size of the alphabet (4 for DNA and RNA sequences and 21 for amino acid sequences). Sequences with a total number of characters shorter than 2 * k + m will be accepted but not all possible patterns of the feature space can be taken into account. When multiple kernels with different k and/or m values should be generated, e.g. for model selection an integer vector can be specified instead of a single numeric values. In this case a list of kernel objects with the individual values from the integer vector of parameter k is generated as result. The processing effort for this kernel is highly dependent on the value of k because of the additional factor 2 in the exponent for the feature space size) and only small values of k will allow efficient processing. Default=1 
m 
maximal number of irrelevant positions between a pair of kmers. The value of m must be an integer value larger than 0. For example a value of m=2 means that zero, one or two irrelevant positions between kmer pairs are considered as valid features. (A value of 0 corresponds to the spectrum kernel with a kmer length of 2*k and is not allowed for the gappy pair kernel). When an integer vector is specified a list of kernels is generated as described above for parameter k. If multiple values are specified both for parameter k and parameter m one kernel object is created for each of the combinations of k and m. Default=1 
r 
exponent which must be > 0 (see details section in spectrumKernel). Default=1 
annSpec 
boolean that indicates whether sequence annotation should
be taken into account (details see on help page for

distWeight 
a numeric distance weight vector or a distance weighting
function (details see on help page for 
normalized 
generated data from 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 
revComplement 
if this parameter is set a kmer pair and its reverse complement are treated as the same feature. Default=FALSE 
mixCoef 
mixing coefficients for the mixture variant of the gappy pair kernel. A numeric vector of length k is expected for this parameter with the unused components in the mixture set to 0. Default=numeric(0) 
kernel 
a sequence kernel object 
x 
one or multiple biological sequences in the form of a

Creation of kernel object
The function 'gappyPairKernel' creates a kernel object for the gappy pair
kernel. This kernel object can then be used with a set of DNA, RNA or
AAsequences to generate a kernel matrix or an explicit representation for
this kernel. The gappy pair kernel uses pairs of neighboring subsequences
of length k (kmers) with up to m irrelevant positions between the kmers. For
sequences shorter than 2*k the self similarity (i.e. the value on the main
diagonal in the square kernel matrix) is 0. The explicit representation
contains only zeros for such a sample. Dependent on the learning task it
might make sense to remove such sequences from the data set as they do not
contribute to the model but still influence performance values.
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
annotationMetadata) and the position dependent variants (for
details see positionMetadata) either in the form of a position
specific or a distance weighted kernel are supported for the gappy
pair kernel. The generation of an explicit representation is not possible
for the position dependent variants of 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.
gappyPairKernel: upon successful completion, the function returns a kernel
object of class GappyPairKernel
.
of getDimFeatureSpace: dimension of the feature space as numeric value
Johannes Palme <[email protected]>
http://www.bioinf.jku.at/software/kebabs
(Mahrenholz, 2011) – C.C. Mahrenholz, I.G. Abfalter, U. Bodenhofer, R. Volkmer
and S. Hochreiter. Complex networks govern coiledcoil oligomerizations 
predicting and profiling by means of a machine learning approach.
(Bodenhofer, 2009) – U. Bodenhofer, K. Schwarzbauer, M. Ionescu and
S. Hochreiter. Modelling position specificity in sequence kernels by fuzzy
equivalence relations.
(Kuksa, 2008) – P. Kuksa, P.H. Huang and V. Pavlovic. Fast Protein Homology
and Fold Detection with Sparse Spatial Sample Kernels
J. Palme, S. Hochreiter, and U. Bodenhofer (2015) KeBABS: an R package
for kernelbased analysis of biological sequences.
Bioinformatics, 31(15):25742576, 2015.
DOI: 10.1093/bioinformatics/btv176.
getKernelMatrix
, getExRep
,
kernelParametersmethod
, spectrumKernel
,
mismatchKernel
, motifKernel
,
GappyPairKernel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  ## instead of user provided sequences in XStringSet format
## for this example a set of DNA sequences is created
## RNA or AAsequences can be used as well with the gappy pair kernel
dnaseqs < DNAStringSet(c("AGACTTAAGGGACCTGGTCACCACGCTCGGTGAGGGGGACGGGGTGT",
"ATAAAGGTTGCAGACATCATGTCCTTTTTGTCCCTAATTATTTCAGC",
"CAGGAATCAGCACAGGCAGGGGCACGGCATCCCAAGACATCTGGGCC",
"GGACATATACCCACCGTTACGTGTCATACAGGATAGTTCCACTGCCC",
"ATAAAGGTTGCAGACATCATGTCCTTTTTGTCCCTAATTATTTCAGC"))
names(dnaseqs) < paste("S", 1:length(dnaseqs), sep="")
## create the kernel object for dimer pairs with up to ten irrelevant
## position between the kmers of the pair without normalization
gappy < gappyPairKernel(k=2, m=10, normalized=FALSE)
## show details of kernel object
gappy
## generate the kernel matrix with the kernel object
km < gappy(dnaseqs)
dim(km)
km[1:5,1:5]
## alternative way to generate the kernel matrix
km < getKernelMatrix(gappy, dnaseqs)
km[1:5,1:5]
## Not run:
## plot heatmap of the kernel matrix
heatmap(km, symm=TRUE)
## End(Not run)

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