getPredProfMixture-methods: Calculation Of Predicition Profiles for Mixture Kernels
In kebabs: Kernel-Based Analysis Of Biological Sequences

Description Usage Arguments Details Value Author(s) References See Also Examples

compute prediction profiles for a given set of biological sequences from a model trained with mixture kernels

## S4 method for signature 'BioVector'
getPredProfMixture(object, trainseqs, mixModel, kernels,
  mixCoef, svmIndex = 1, sel = 1:length(object),
  weightLimit = .Machine$double.eps)

## S4 method for signature 'XStringSet'
getPredProfMixture(object, trainseqs, mixModel, kernels,
  mixCoef, svmIndex = 1, sel = 1:length(object),
  weightLimit = .Machine$double.eps)

## S4 method for signature 'XString'
getPredProfMixture(object, trainseqs, mixModel, kernels,
  mixCoef, svmIndex = 1, sel = 1, weightLimit = .Machine$double.eps)

`object`	a single biological sequence in the form of an `DNAString`, `RNAString` or `AAString` or multiple biological sequences as `DNAStringSet`, `RNAStringSet`, `AAStringSet` (or as `BioVector`).
`trainseqs`	training sequences on which the mixture model was trained as `DNAStringSet`, `RNAStringSet`, `AAStringSet` (or as `BioVector`).
`mixModel`	model object of class `KBModel` trained with kernel mixture.
`kernels`	a list of sequence kernel objects of class `SequenceKernel`. The same kernels must be used as in training.
`mixCoef`	mixing coefficients for the kernel mixture. The same mixing coefficient values must be used as in training.
`svmIndex`	integer value selecting one of the pairwise SVMs in case of pairwise multiclass classification. Default=1
`sel`	subset of indices into `x` as integer vector. When this parameter is present the prediction profiles are computed for the specified subset of samples only. Default=`integer(0)`
`weightLimit`	the feature weight limit is a single numeric value and allows pruning of feature weights. All feature weights with an absolute value below this limit are set to 0 and are not considered for the prediction profile computation. This parameter is only relevant when feature weights are calculated in KeBABS during training. Default=.Machine$double.eps

With this method prediction profiles can be generated explicitely for a given set of sequences with a model trained on a precomputed kernel matrix as mixture of multiple kernels.

upon successful completion, the function returns a set of prediction profiles for the sequences as class PredictionProfile.

Johannes Palme <kebabs@bioinf.jku.at>

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 coiled coil oligomerization - predicting and profiling by means of a machine learning approach.

(Bodenhofer, 2009) – U. Bodenhofer, K. Schwarzbauer, S. Ionescu, and S. Hochreiter. Modeling Position Specificity in Sequence Kernels by Fuzzy Equivalence Relations.

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.

PredictionProfile, predict, plot, featureWeights, getPredictionProfile

## set random generator seed to make the results of this example
## reproducable
set.seed(123)

## load coiled coil data
data(CCoil)
gappya1 <- gappyPairKernel(k=1,m=11, annSpec=TRUE)
gappya2 <- gappyPairKernel(k=2,m=9, annSpec=TRUE)
kernels <- list(gappya1, gappya2)
mixCoef <- c(0.7,0.3)

## precompute mixed kernel matrix
km <- as.KernelMatrix(mixCoef[1]*gappya1(ccseq) +
                      mixCoef[2]*gappya2(ccseq))
mixModel <- kbsvm(x=km, y=as.numeric(yCC),
               pkg="e1071", svm="C-svc", cost=15)

## define two new sequences to be predicted
GCN4 <- AAStringSet(c("MKQLEDKVEELLSKNYHLENEVARLKKLV",
                      "MKQLEDKVEELLSKYYHTENEVARLKKLV"))
names(GCN4) <- c("GCN4wt", "GCN_N16Y,L19T")
## assign annotation metadata
annCharset <- annotationCharset(ccseq)
annot <- c("abcdefgabcdefgabcdefgabcdefga",
           "abcdefgabcdefgabcdefgabcdefga")
annotationMetadata(GCN4, annCharset=annCharset) <- annot

## compute prediction profiles
predProf <- getPredProfMixture(GCN4, ccseq, mixModel,
                               kernels, mixCoef)

## show prediction profiles
predProf

## plot prediction profile of both aa sequences
plot(predProf, sel=c(1,2), ylim=c(-0.4, 0.2), heptads=TRUE, annotate=TRUE)