getPredProfMixture,BioVector-method {kebabs} | R Documentation |
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$double.eps)
object |
a single biological sequence in the form of an
|
trainseqs |
training sequences on which the mixture model was
trained as
|
mixModel |
model object of class |
kernels |
a list of sequence kernel objects of class
|
mixCoef |
mixing coefficients for the kernel mixture. The same mixing coefficient values must be used as in training. |
svmIndex |
integer value selecting one of the pairwise SVMs in case of pairwise multiclass classification. Default=1 |
sel |
subset of indices into |
weightLimit |
the feature weight limit is a single numeric value and allows pruning of feature weights. All feature weights with an absolute value below this limit are set to 0 and are not considered for the prediction profile computation. This parameter is only relevant when feature weights are calculated in KeBABS during training. Default=.Machine$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)