[Bioc-devel] normalize.loess effizient method

[lgautier at altern.org]

Thanks a lot Markus.

To facilitate our work (review and inclusion), could you directly post a
patch for your changes ?

(Something like svn diff > fixnormalizeloess.patch should make it).


Thanks,


L.





> 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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