NormalizeRUVRandClust: RUV-random method for clustering

In MetNorm: Statistical Methods for Normalizing Metabolomics Data

Description

Given suitable controls and user input, this function may be used to obtain a normalized metabolomics data matrix suitable for clustering

Usage

NormalizeRUVRandClust(RUVRand,
                              maxIter, 
                              nUpdate=maxIter+1, 
                              lambdaUpdate=TRUE, 
                              p=p,...)

Arguments

RUVRand	Output from NormalizeRUVRand
maxIter	Maximum number of iterations
nUpdate	Update the unwanted variation component every nUpdate iterations
lambdaUpdate	A logical indicating whether the regularization parameter needs to be updated
p	The number of clusters to be used in the k-means clustering
...	Other arguments for kmeans

Author(s)

Alysha M De Livera and Laurent Jacob

References

De Livera, A. M., Dias, D. A, De Souza, D., Rupasinghe, T., Pyke, J., Tull, D., Roessner, U., McConville, M., and Speed, T. P. (2012). Normalizing and integrating metabolomics data. Analytical chemistry, 84(24), 10768-76.

De Livera, A.M., Aho-Sysi, M., Jacob, L., Gagnon-Bartch, J., Castillo, S., Simpson, J.A., and Speed, T.P. (2014), Statistical methods for handling unwanted variation in metabolomics data

Examples

data(UV)
Y<-data.matrix(UV[,-c(1:3)]) 

##Empirical controls
IS<-Y[,which(colnames(Y)=="IS")] 
r<-numeric(dim(Y)[2])
for(j in 1:length(r)){
  r[j]<-cor(IS,Y[,j])
}
ctl<-logical(length(r))
ctl[which(r>round(quantile(r,0.7),2))]<-TRUE 

## Not run:   
ruv<-NormalizeRUVRand(Y=Y,ctl=ctl,k=3) 
ruvclust<-NormalizeRUVRandClust(RUVRand=ruv,
                   maxIter=200, 
                   nUpdate=100,
                   lambdaUpdate=TRUE,
                   p=2)
ruvclustY<-ruvclust$newY
pairs(princomp(ruvclustY,cor=TRUE)$scores[,c(1:3)],
      pch=as.numeric(UV[,2]), col=UV[,3],
      main="RUV random for clustering")

## End(Not run)

MetNorm documentation built on May 2, 2019, 4:16 a.m.