getPredProfMixture,BioVector-method {kebabs} | R Documentation

## Calculation Of Predicition Profiles for Mixture Kernels

### Description

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

### Usage

    ## 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)

### Arguments

`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

### Details

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.

### Value

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

### Author(s)

Johannes Palme <kebabs@bioinf.jku.at>

### References

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.

### See Also

`PredictionProfile`, `predict`, `plot`, `featureWeights`, `getPredictionProfile`

### Examples

    ## 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)

[Package kebabs version 1.18.0 Index]