lmw: Locally Moderated Weighted-t.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/PLWandLMW.R

Description

Computes Locally Moderated Weighted t-test for microarray data.

Usage

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lmw(x,design=rep(1,ncol(x)),contrast=matrix(1), meanX=NULL,
    maxIter = 200, epsilon = 1e-06, verbose = TRUE,
    nknots = 10, nOut = 2000, nIn = 4000, knots = NULL,
    checkRegulation = TRUE)

Arguments

x

Data, log2 expression indexes.

design

design matrix

contrast

contrast matrix

meanX

Covariate used to model scale parameter, default=NULL (see details)

maxIter

maximum number of iterations

epsilon

convergence criteria

verbose

print computation info or not

nknots

Number of knots of spline for v

nOut

Parameter for calculating knots, see getKnots

nIn

Parameter for calculating knots, see getKnots

knots

Knots, if not NULL it overrides nknots, nOut and nIn

checkRegulation

If TRUE, data is checked for a correct specified contrast (see details)

Details

This function computes the Locally Moderated Weighted-t statistic (LMW) described in Astrand (2007b), thus calculating locally moderated weighted t-statistic, p-value and log2(FC) for each row of the data matrix x.

Each gene g (row of x) is modeled as:

y_g|c_g ~ N(mu_g,c_g*Sigma)

c ~ InvGamma(m/2,m*v/2)

where v is function of the mean intensity: v(mean(mu_g)), N denotes a multivariate normal distribution, Sigma is a covariance matrix and InvGamma(a,b) is the inverse-gamma distribution with density function

f(x)=b^a exp{-b/x} x^{-a-1} /Gamma(a)

Given the design matrix D, mu_g equals D*gamma_g, and given the contrast matrix C the hypothesis C*gamma_g=0 is tested. C should be a one row matrix of same length as the column vector gamma_g.

See examples on how to specify the design and contrast matrices.

A cubic spline is used to parameterize the smooth function v(x)

v(x)=exp{H(x)^T beta}

where H:R->R^(2p-1) is a set B-spline basis functions for a given set of p interior spline-knots, see chapter 5 of Hastie et al. (2001).

For details about the model see Kristiansson et al. (2005), Astrand et al. (2007a,2007b).

As specified above, v is modeled as a function of mean intensity: v(mean(mu_g)). If the parameter meanX is not NULL, meanX is used instead of the mean intensity when modeling v. Thus, if meanX is not NULL, meanX must be a vector of length equal to the number of rows of the data matrix x.

The parameter estimation procedure is based on the assumption that the specified contrast is close to zero for most genes, or at least that the median contrast over all genes is close to zero. A check is run on data to validate this assumptions. If the checking fails, with the error message "warning: most genes appears to be regulated..." and if YOU ARE SURE that the design and contrast is correct, use checkRegulation=FALSE.

Value

Sigma

Estimated covariance matrix for y=P' x

m

Estimated shape parameter for inverse-gamma prior for gene variances

v

Estimated scale parameter curve for inverse-gamma prior for gene variances

converged

T if the EM algorithms converged

iter

Number of iterations

modS2

Moderated estimator of gene-specific variances

histLogS2

Histogram of log(s2) where s2 is the ordinary variance estimator

fittedDensityLogS2

The fitted density for log(s2)

logs2

Variance estimators, logged with base 2.

t

Moderated t-statistic

coefficients

Estimated contrast

p.value

P-value from the moderated t-statistic

dfT

Degrees of freedom of the moderated t-statistic

weights

Weights for estimating the contrast

P

Transformation matrix

beta

Estimated parameter vector beta of spline for v(x)

knots

The knots used in spline for v(x)

Author(s)

Magnus Astrand

References

Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, volume 1. Springer, first edition.

Kristiansson, E., Sjogren, A., Rudemo, M., Nerman, O. (2005). Weighted Analysis of Paired Microarray Experiments. Statistical Applications in Genetics and Molecular Biology 4(1)

Astrand, M. et al. (2007a). Improved covariance matrix estimators for weighted analysis of microarray data. Journal of Computational Biology, Accepted.strand

Astrand, M. et al. (2007b). Empirical Bayes models for multiple-probe type arrays at the probe level. Bioinformatics, Submitted 1 October 2007.

See Also

estimateSigmaMVbeta, plw

Examples

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# ------------------------------------------
# Example analyzing the 6 arrays in the 
# AffySpikeU95Subset data set

# Loading the data
data(AffySpikeU95Subset)

# Defining design and contrast matrix
group<-factor(rep(1:2,each=3))
design<-model.matrix(~group-1)
contrast<-matrix(c(1,-1),1,2)

# Computing RMA expression index
data.rma<-exprs(rma(AffySpikeU95Subset))

# Analyzing
model1<-lmw(data.rma,design=design,contrast=contrast,epsilon=0.01)

## Look at fitted vs observed density for log(s2)
varHistPlot(model1)

## Look at fitted curve for scale parameter
scaleParameterPlot(model1)

plw documentation built on May 2, 2018, 4:16 a.m.