# [Bioc-devel] normalize.loess effizient method

Laurent Gautier lgautier at gmail.com
Tue Jul 1 19:05:16 CEST 2008

```Markus,

Thanks for your patch. It is available in the development branch
(future release 2.3).

L.

2008/6/23 Markus Schmidberger <schmidb at ibe.med.uni-muenchen.de>:
> Hello,
>
> I parallelized the normalize loess function (see affyPara package). Thereby
> I found a memory inefficient implementation of normalize.loess.
> The matrices fs and newdata are of the size of the complete intensity matrix
> and not necessary.  I removed this two matrices and the oldfs matrix.
> Attached the changed code.
>
> Best
> Markus
>
>
> normalize.loess <- function(mat, subset=sample(1:(dim(mat)[1]), min(c(5000,
> nrow(mat)))),
>       epsilon=10^-2, maxit=1, log.it=TRUE, verbose=TRUE, span=2/3,
>       family.loess="symmetric"){
>     J <- dim(mat)[2]
>   II <- dim(mat)[1]
>   if(log.it)
>       mat <- log2(mat)
>     change <- epsilon +1
>   iter <- 0
>   w <- c(0, rep(1,length(subset)), 0) ##this way we give 0 weight to the
>   ##extremes added so that we can interpolate
>     while(iter < maxit){
>       iter <- iter + 1
>       means <- matrix(0,II,J) ##contains temp of what we substract
>             for (j in 1:(J-1)){
>           for (k in (j+1):J){
>               y <- mat[,j] - mat[,k]
>               x <- (mat[,j] + mat[,k]) / 2
>               index <- c(order(x)[1], subset, order(-x)[1])
>               ##put endpoints in so we can interpolate
>               xx <- x[index]
>               yy <- y[index]
>               aux <-loess(yy~xx, span=span, degree=1, weights=w,
> family=family.loess)
>               aux <- predict(aux, data.frame(xx=x)) / J
>               means[, j] <- means[, j] + aux
>               means[, k] <- means[, k] - aux
>               if (verbose)
>                   cat("Done with",j,"vs",k," in iteration ",iter,"\n")
>           }
>       }
>       mat <- mat - means
>       change <- max(colMeans((means[subset,])^2))
>             if(verbose)
>           cat(iter, change,"\n")
>     }
>     if ((change > epsilon) & (maxit > 1))
>       warning(paste("No convergence after", maxit, "iterations.\n"))
>     if(log.it) {
>       return(2^mat)
>   } else
>       return(mat)
> }
>
> --
> Dipl.-Tech. Math. Markus Schmidberger
>
> Ludwig-Maximilians-Universität München
> IBE - Institut für medizinische Informationsverarbeitung,
> Biometrie und Epidemiologie
> Marchioninistr. 15, D-81377 Muenchen
> URL: http://ibe.web.med.uni-muenchen.de Mail: Markus.Schmidberger [at]
> ibe.med.uni-muenchen.de
>
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>

```