Package 'krm'

Amino Acid Properties

Description

Amino Acid Properties

Format

A data frame with 20 observations on the following 13 variables.

Symbol

a chacracter vector with 20 values: A through Y

AA_Name

a chacracter vector with 20 values: Alanine through Tyrosine

AA_Symbol

a chacracter vector with 20 values: Ala through Tyr

Surface_Area_Chothia

a numeric vector

Residue_Volume_Zamayatin

a numeric vector

Bulkiness_Jones

a numeric vector

Polarity_Jones

a numeric vector

Refractivity_Jones

a numeric vector

Hydrophobicity_Engleman

a numeric vector

Hydrophobicity_Prabha

a numeric vector

Hydrophilicity_Hopp

a numeric vector

Hydrophilicity_Levitt

a numeric vector

RelMutability_Jones

a numeric vector

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Functions Related to Sequence Alignment

Description

Functions related to sequence alignment

Usage

calcPairwiseIdentity(alignment, dissimilarity, removeGap)
alignment2count (alignment, level=20, weight=rep(1,nrow(alignment)))
alignment2trancount (alignment, weight=rep(1,nrow(alignment)))
removeGap (seq)

Arguments

alignment	matrix of arabic representation of sequences (1 based)
dissimilarity	Boolean.
removeGap	Boolean
level	integer. Size of alphabet
weight	numeric vector. Weights given to each sequence
seq	string. A string of amino acids

Value

alignment2count return T by 20 matrix, where T is the number of column in the alignment. alignment2trancount return a T by 4 matrix, each row is the count of MM, MD, DM, DD for each position.

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A Transformation of Chi-squared Random Variable

Description

A transformation of Chi-squared random variable to make it normal like.

Usage

chi.norm(q, v)

Arguments

q	numeric. A random variable following chi-squared distribution
v	numeric. A random variable following normal distribution

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9-Component Mixture Dirichlet Prior for Protein Sequences

Description

9-Component Mixture Dirichlet Prior for Protein Sequences

Format

List of 2. The alpha element is a 9 by 20 matrix, where each row represents one Dirichlet distribution of 20 dimensions. The mix.coef element contains the mixing probability, a vector 0f 9 numbers that add up to 1.

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Functions related to mixture Dirichlet distribution

Description

Functions related to mixture Dirichlet distribution

Usage

dmdirichlet(x, mAlpha, mixtureCoef)
ddirichlet (x, alpha)
rdirichlet (n, alpha) 
rmdirichlet (mAlpha, mixtureCoef) 
modifyDirichlet (prior, y) 
logIntegrateMixDirichlet(y, prior, tau=1) 
logIntegrateDirichlet (y, alpha)

Arguments

x	A vector containing a single deviate or matrix containing one random deviate per row.
mAlpha	matrix. Each row is a parameter of Dirichlet
alpha	numeric vector. Parameter for a Dirichlet distribution
mixtureCoef	numeric vector
n	integer
prior	list of two components: alpha and mix.coef
y	numeric vector of counts
tau	numeric

Details

ddirichlet andn rdirichlet are identically copied from MCMCpack

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Protein Sequence Kernels

Description

Get mutual information and other kernels for protein sequences

Usage

getSeqKernel (sequences, kern.type=c("mm", "prop", "mi"), tau, call.C=TRUE
    , seq.start=NULL, seq.end=NULL)

Arguments

sequences	String or list. If string, the name of a fasta file containing aligned sequences. If list, a list of strings, each string is a protein sequence. If list, call.C will be set to FALSE internally because C/C++ function needs sequence file name as input
kern.type	string. Type of kernel. mm: match-mismatch, prop: physicochemical properties, mi: mutual information.
tau	Numeric. It is the same as rho^-2.
call.C	Boolean. If TRUE, do a .C call. If FALSE, the implementation is in R. The .C call is 50 times faster.
seq.start	integer. Start position of subsequence to be used in computing kernel.
seq.end	integer. End position of subsequence to be used in computing kernel.

Details

call.C option is to allow comparison of R and C implementation. The two should give the same results and C implementation is 50 times faster.

when kern.type is mi and call.C is TRUE and when running on linux, this function will print messages like "read ...". This message is generated from U::openRead

Examples

fileName=paste(system.file(package="krm")[1],'/misc/SETpfamseed_aligned_for_testing.fasta',
     sep="")
K=getSeqKernel (fileName, kern.type="mi", tau=1, call.C=TRUE)
K

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Functions related to profile HMM

Description

Functions related to profile HMM

Usage

hmmMargLlik(dat, aaPrior, tau)
readPriorFromFile(priorFileName)

Arguments

dat	a matrix representation of a multiple sequence alignment, each row is a sequence, each column is a position
aaPrior	a list of two elements, "alpha" "mix.coef", representing mixture Dirichlet prior
tau	numeric
priorFileName	string

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Kernel-based Regression Models

Description

Implements tests for kernel-based regression model. The main function is krm.most(). Both Euclidean and protein sequence covariates can be used to form kernels.

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Kernel-based Regression Model Maximum of adjusted Score Test

Description

Performs maximum of adjusted score test for kernel-based regression models. Both Euclidean and protein sequence covariates can be used to form kernels.

Usage

krm.most (formula, data, regression.type=c("logistic","linear"), 
    kern.type=c("rbf","mi","mm","prop"), n.rho=10, range.rho=0.99, 
    n.mc=2000, 
    seq.file.name=NULL, formula.kern=NULL, seq.start=NULL, seq.end=NULL,
    inference.method=c("parametric.bootstrap", "perturbation", "Davies"),
    verbose=FALSE)

Arguments

formula	a formula object describing the null model
data	data frame
regression.type	logistic regression or linear regression
kern.type	rbf: radial basis function kernel, a kernel type for Euclidean covariates. The other three kernels are for protein sequence covariates (Fong et al. 2014). mm: match-mismatch, prop: physicochemical properties, mi: mutual information,
n.rho	integer. Number of rhos to maximize over
range.rho	numeric. A number between 0 and 1. It controls the range of rhos to use to compute kernel
seq.file.name	There are two ways to provide protein sequence information. One is to supply a sequence file named 'seq.file.name', which contains sequences in fasta format. Two is to supply a formula through formula.kern, and the variable name should be part of data.
formula.kern	The formula for the covariates used to form the kernel. It may specify Euclidean covariates or a string covariate that contains protein sequences.
seq.start	integer. Start position of subsequence to be used in computing kernel. Only supported when the sequence is specified through formula.kern.
seq.end	integer. End position of subsequence to be used in computing kernel. Only supported when the sequence is specified through formula.kern.
n.mc	integer. Number of bootstrap samples used to compute p-values.
inference.method	parametric.bootstrap implements methods from Fong et al. (2014). perturbation uses methods from Wu et a. (2013) Davies uses the upper bound method from Davies (1987) and Liu et al. (2008).
verbose	boolean

Value

A list of class krm

p.values

If inference.method=="Davies", a single p-value. If inference.method=="perturbation" or "parametric.bootstrap", a vector of four p-values, named chiI, chiII, normI, normII. For perturbation, chiII and normII are NA. chiI/chiII p-values are based on chi-squared approximation and normI/normII are based on normal approximations. chiI/normI p-values are based on plugin estimator of mean and variance of score statistic, chiII/normII are based on modified estimator of mean and variance of score statistic. chiII or normII are more powerful than chiI and normI. For more details, see Fong et al. (2014)

References

Fong, Y. and Datta, S. and Georgiev, I. and Kwong, P. and Tomaras, G. (2014) Kernel-based Logistic Regression Model for Protein Sequence Without Vectorialization, Biostatistics.

Wu et al. (2013) Kernel Machine SNP-Set Testing Under Multiple Candidate Kernels, Genetic epidemiology.

Davies, R. (1987) Hypothesis testing when a nuisance parameter is present only under the alternative, Biometrika, 74, 33-43.

Liu, D., Ghosh, D. and Lin, X. (2008) Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models, BMC Bioinformatics, 9, 292.

Examples

# in addition to the examples listed here, there are more examples 
# under folder R/library/krm/unitTests

## Not run: 
# the examples are not run during package build because it takes a little too long to run

# an Euclidean kernel example from Liu et al. (2008)
data=sim.liu.2008 (n=100, a=.1, seed=1) 
test = krm.most(y~x, data, formula.kern=~z.1+z.2+z.3+z.4+z.5, kern.type="rbf")


# a protein sequence kernel example
dat.file.name=paste(system.file(package="krm")[1],'/misc/y1.txt', sep="") 
seq.file.name=paste(system.file(package="krm")[1],'/misc/sim1.fasta', sep="")
dat=read.table(dat.file.name); names(dat)="y"
test = krm.most (y~1, dat, seq.file.name=seq.file.name, kern.type="mi")



## End(Not run)

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Adjusted Score Test

Description

Adjusted score test for kernel-based regression models. This function is typically not used directly, but is called within krm.most().

Usage

krm.score.test(formula, data, K, regression.type=c("logistic","linear"), verbose=FALSE)

Arguments

formula	a formula object. Model under null.
data	a data frame
K	a n by n kernel/correlation matrix
regression.type	a string
verbose	Boolean

Examples

dat=sim.liu.2008(n=100, a=0, seed=1)
z=as.matrix(subset(dat, select=c(z.1,z.2,z.3,z.4,z.5)))
rho=1
K=kyotil::getK(z,kernel="rbf",para=rho^-2)
krm.score.test (y~x, dat, K, regression.type="logistic")

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Read a Fasta Sequence File

Description

Read a Fasta Sequence File

Usage

readFastaFile(fileName, sep = " ")
writeFastaFile (seqList, fileName)
aa2arabic (seq1) 
string2arabic (seqList) 
fastaFile2arabicFile (fastaFile, arabicFile, removeGapMajor=FALSE)
selexFile2arabicFile (selexFile, arabicFile, removeGapMajor=FALSE)
stringList2arabicFile (seqList, arabicFile, removeGapMajor=FALSE)
arabic2arabicFile (alignment, arabicFile) 
readSelexFile (fileName)
readSelexAsMatrix (fileName) 
arabic2fastaFile (alignment, fileName)
readArabicFile (fileName) 
readBlockFile (fileName)

Arguments

fileName	string
fastaFile	string
arabicFile	string
selexFile	string
sep	string
seq1	string. A string of amino acids
seqList	list of string.
removeGapMajor	Boolean
alignment	matrix of arabic representation of sequences (1 based)

Value

string2arabic returns a matrix of arabic numbers representing aa. readSelexFile return a list of strings. readArabicFile return a matrix of n by p alignment.

Examples

library(RUnit)
fileName=paste(system.file(package="krm")[1],'/misc/SETpfamseed_aligned_for_testing.fasta', sep="")
seqs = readFastaFile (fileName, sep=" ")
checkEquals(length(seqs),11)

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Simulate sDataset

Description

Per Liu et al (2008) and Liu et al (2007)

Usage

sim.liu.2008(n, a, seed = NULL)
sim.liu.2007(n, a, seed = NULL)

Arguments

n	sample size
a	numeric. If a is 0, then the data is used to study size, otherwise power
seed	optional random number generator seed

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