S4 class for representing coiled coil prediction results
Objects from the Class
In principle, objects of this class can be created by calls of
new("CCProfile"), although it is not advised to do so.
Most importantly, the
predict function of
returns its results in objects of this type.
This class extends the class
the kebabs package directly and therefore inherits all its slots
and methods. The following slots are defined for
Object of class
numericcontaining the discriminant function value(s) (see
Object of class
factorcontaining the final classification(s). Upon a call to
predict, it is either “trimer” or “dimer”.
As described in
CCModel, the discriminant function
of the coiled coil classifier is essentially a weighted sum of
numbers of occurrences of certain patterns in the sequence under
consideration, i.e. every pattern occurring in the sequence contributes
a certain weight to the discriminant function. Since every such
occurrence is uniquely linked to two specific residues in the
sequence, every amino acid in the sequence contributes a unique weight
to the discriminant function value which is nothing else but half the
sum of weights of matching patterns in which this amino acid is
involved. If we denote the contribution of each position i with
si(x), it follows immediately that
f(x)=b+sum over all si(x) for i=1,… L,
where L is the length of the sequence x. The values
si(x) can then be understood as the contributions that
the i-th residue makes to the overall classification of the sequence
x, which we call prediction profile. These profiles can
either be visualized as they are without taking the offset b
into account or by distributing b equally over all residues.
These are the so-called baselines that are included in
CCProfile objects. They are computed as -b / L.
signature(x="CCProfile", y="missing"): see
signature(x="CCProfile", y="missing"): if the
xcontains the profiles of at least three sequences, the profiles are visualized as a heatmap. This method is inherited from the kebabs package; for details, see
signature(object="CCProfile"): displays the most important information stored in the
object, such as, the sequences, kernel parameters, baselines, profiles, and classification results.
CCProfile class inherits all accessors from the
PredictionProfile class, such as,
the indexing operator
Additionally, the procoil package defines the following two methods:
signature(fitted="CCProfile"): for compatibility with previous versions, a method
profileis available, too. It extracts the profile(s) in the same way as
signature(object="CCProfile"): extracts the final classifications. This function returns a factor with levels “dimer” and “trimer”. If
decision.values=TRUEis specified, a numeric vector is attached to the result as an attribute
"decision.values"which also contains the discriminant function values.
Ulrich Bodenhofer email@example.com
Mahrenholz, C.C., Abfalter, I.G., Bodenhofer, U., Volkmer, R., and Hochreiter, S. (2011) Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics 10(5):M110.004994. DOI: 10.1074/mcp.M110.004994
Palme, J., Hochreiter, S., and Bodenhofer, U. (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics 31(15):2574-2576. DOI: 10.1093/bioinformatics/btv176
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 31 32 33 34 35 36 37
showClass("CCProfile") ## predict oligomerization of GCN4 wildtype GCN4wt <- predict(PrOCoilModel, "MKQLEDKVEELLSKNYHLENEVARLKKLV", "abcdefgabcdefgabcdefgabcdefga") ## display summary of result GCN4wt ## show raw prediction profile profile(GCN4wt) ## plot profile plot(GCN4wt) ## define four GCN4 mutations GCN4mSeq <- c("GCN4wt" ="MKQLEDKVEELLSKNYHLENEVARLKKLV", "GCN4_N16Y_L19T"="MKQLEDKVEELLSKYYHTENEVARLKKLV", "GCN4_E22R_K27E"="MKQLEDKVEELLSKNYHLENRVARLEKLV", "GCN4_V23K_K27E"="MKQLEDKVEELLSKNYHLENEKARLEKLV") GCN4mReg <- rep("abcdefgabcdefgabcdefgabcdefga", 4) ## predict oligomerization GCN4mut <- predict(PrOCoilModel, GCN4mSeq, GCN4mReg) ## display summary of result GCN4mut ## display predictions fitted(GCN4mut) ## overlay plot of two profiles plot(GCN4mut[c(1, 2)]) ## show heatmap heatmap(GCN4mut)
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.