Last data update: 2014.03.03

 R: Class "CCProfile"
CCProfile-classR Documentation

Class "CCProfile"

Description

S4 class for representing coiled coil prediction results

Objects from the Class

In principle, objects of this class can be created by calls of the form new("CCProfile"), although it is not advised to do so. Most importantly, the predict function of returns its results in objects of this type.

Slots

This class extends the class PredictionProfile from the kebabs package directly and therefore inherits all its slots and methods. The following slots are defined for CCProfile objects additionally:

disc:

Object of class numeric containing the discriminant function value(s) (see CCModel for details)

pred:

Object of class factor containing the final classification(s). Upon a call to predict, it is either “trimer” or “dimer”.

Prediction profiles

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.

Methods

plot

signature(x="CCProfile", y="missing"): see plot

heatmap

signature(x="CCProfile", y="missing"): if the CCProfile object x contains 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 heatmap.

show

signature(object="CCProfile"): displays the most important information stored in the CCProfile object object, such as, the sequences, kernel parameters, baselines, profiles, and classification results.

Accessor-like methods

The CCProfile class inherits all accessors from the PredictionProfile class, such as, sequences, baselines, profiles, and the indexing operator x[i]. Additionally, the procoil package defines the following two methods:

profile

signature(fitted="CCProfile"): for compatibility with previous versions, a method profile is available, too. It extracts the profile(s) in the same way as profiles

fitted

signature(object="CCProfile"): extracts the final classifications. This function returns a factor with levels “dimer” and “trimer”. If decision.values=TRUE is specified, a numeric vector is attached to the result as an attribute "decision.values" which also contains the discriminant function values.

Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

References

http://www.bioinf.jku.at/software/procoil/

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

See Also

CCModel, plot, plot, PredictionProfileAccessors,

Examples

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(procoil)
Loading required package: kebabs
Loading required package: Biostrings
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
Loading required package: kernlab

Attaching package: 'kernlab'

The following object is masked from 'package:Biostrings':

    type

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/procoil/CCProfile-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: CCProfile-class
> ### Title: Class "CCProfile"
> ### Aliases: CCProfile-class CCProfile [,CCProfile,index,ANY,ANY-method
> ###   show,CCProfile-method heatmap,CCProfile,missing-method
> ###   baselines,CCProfile-method profiles,CCProfile-method
> ###   profile,CCProfile-method sequences,CCProfile-method
> ###   fitted,CCProfile-method show.CCProfile heatmap.CCProfile
> ###   baselines.CCProfile profiles.CCProfile profile.CCProfile
> ###   sequences.CCProfile fitted.CCProfile
> ### Keywords: classes
> 
> ### ** Examples
> 
> showClass("CCProfile")
Class "CCProfile" [package "procoil"]

Slots:
                                                                  
Name:       disc      pred sequences baselines  profiles    kernel
Class:   numeric    factor       ANY   numeric       ANY       ANY

Extends: "PredictionProfile"
> 
> ## predict oligomerization of GCN4 wildtype
> GCN4wt <- predict(PrOCoilModel,
+                   "MKQLEDKVEELLSKNYHLENEVARLKKLV",
+                   "abcdefgabcdefgabcdefgabcdefga")
> 
> ## display summary of result
> GCN4wt
An object of class  "CCProfile" 

Sequence:

  A AAVector instance of length 1 
    width seq                                              
[1]    29 MKQLEDKVEELLSKNYHLENEVARLKKLV                                         

gappy pair kernel: k=1, m=5, annSpec=TRUE

Baseline:  0.03698699 

Profile:
          Pos 1        Pos 2            Pos 28       Pos 29 
[1]  0.140762197  0.024184153  ... -0.023390414  0.095688066


Predictions:
          Score  Class
[1] -0.158713692  dimer

> 
> ## show raw prediction profile
> profile(GCN4wt)
         Pos 1      Pos 2       Pos 3     Pos 4     Pos 5     Pos 6     Pos 7
[1,] 0.1407622 0.02418415 0.002011822 0.1414524 0.1369286 0.1145901 0.1714275
         Pos 8      Pos 9     Pos 10     Pos 11     Pos 12    Pos 13    Pos 14
[1,] 0.2617676 0.02745403 -0.0211955 -0.1319729 0.01365944 0.1039332 0.1122011
         Pos 15     Pos 16     Pos 17     Pos 18     Pos 19     Pos 20
[1,] -0.3855509 0.01650569 0.06290137 0.01949051 0.06539256 0.00837083
          Pos 21     Pos 22     Pos 23      Pos 24     Pos 25     Pos 26
[1,] -0.07611438 0.09560804 0.01451723 -0.03949032 -0.1438368 0.04703749
         Pos 27      Pos 28     Pos 29
[1,] 0.05957645 -0.02339041 0.09568807
> 
> ## 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
An object of class  "CCProfile" 

Sequences:

  A AAVector instance of length 4 
    width seq                                               names               
[1]    29 MKQLEDKVEELLSKNYHLENEVARLKKLV                     GCN4wt              
[2]    29 MKQLEDKVEELLSKYYHTENEVARLKKLV                     GCN4_N16Y_L19T      
[3]    29 MKQLEDKVEELLSKNYHLENRVARLEKLV                     GCN4_E22R_K27E      
[4]    29 MKQLEDKVEELLSKNYHLENEKARLEKLV                     GCN4_V23K_K27E      

gappy pair kernel: k=1, m=5, annSpec=TRUE

Baselines:  0.03698699 0.03698699 0.03698699 0.03698699 

Profiles:
                      Pos 1        Pos 2            Pos 28       Pos 29 
        GCN4wt  0.140762197  0.024184153  ... -0.023390414  0.095688066
GCN4_N16Y_L19T  0.144175109  0.024770521  ... -0.023957537  0.098008113
GCN4_E22R_K27E  0.137580719  0.023637548  ... -0.042715408  0.148626188
GCN4_V23K_K27E  0.141592659  0.024326834  ... -0.047527521  0.152960221


Predictions:
                            Score  Class
              GCN4wt -0.158713692  dimer
      GCN4_N16Y_L19T  0.420763995 trimer
      GCN4_E22R_K27E  0.623458294 trimer
      GCN4_V23K_K27E -0.500406810  dimer

> 
> ## display predictions
> fitted(GCN4mut)
        GCN4wt GCN4_N16Y_L19T GCN4_E22R_K27E GCN4_V23K_K27E 
         dimer         trimer         trimer          dimer 
Levels: dimer trimer
> 
> ## overlay plot of two profiles 
> plot(GCN4mut[c(1, 2)])
> 
> ## show heatmap
> heatmap(GCN4mut)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO