self.normalization <- function(madat,madat.swap,lowess.f=2/3,flaglevel=-200) {
  spots.validos <-  madat$intensities$flags > flaglevel & madat.swap$intensities$flags > flaglevel
  madat$intensities <- madat$intensities[spots.validos,]
  madat.swap$intensities <- madat.swap$intensities[spots.validos,]
 
  c <- lowess(0.5*(madat$intensities$a + madat.swap$intensities$a),0.5*(madat$intensities$m + madat.swap$intensities$m),f=lowess.f)$y

  madat$intensities$m <- madat$intensities$m - c
  madat$norm <- paste('Autonormalização (f=',format(lowess.f,digits=3),')',sep='')

  madat
}

within.slide.scale <- function(madat) {
   madat -> scaled
   totals <- tapply(madat$intensities$m,madat$intensities$block,mad)
   denom <- prod(totals)^(1/length(totals))
   fatores <- totals/denom

   for(i in 1:length(totals)) {
     scaled$intensities[scaled$intensities$block == i,]$m <- scaled$intensities[scaled$intensities$block == i,]$m / fatores[[i]]
   }

   scaled
}

subarray.lowess <- function(madat,lowess.f=2/3,flaglevel=-200) {
 madat$intensities <- subset(madat$intensities,flags > flaglevel)
 norm <- madat

 for(i in 1:max(madat$intensities$block)) {
      subarray <- subset(madat$intensities,block == i,select=c(m,a))
      c <- lowess(subarray$a,subarray$m,f=lowess.f)$y
      norm$intensities$m[norm$intensities$block == i] <- norm$intensities$m[norm$intensities$block == i] - c
 }

 norm$norm <- paste('Lowess por Subarray (f=',format(lowess.f,digits=3),')',sep='')
 norm
}


global.lowess <- function(madat,lowess.f=2/3,flaglevel=-200) {
 madat$intensities <- subset(madat$intensities,flags > flaglevel)
 norm <- madat

 c <- lowess(madat$intensities$a,madat$intensities$m,f=lowess.f)$y
 norm$intensities$m <- norm$intensities$m - c
 norm$norm <- paste('Lowess Global (f=',format(lowess.f,iter=lowess.iter,digits=3),')',sep='')
 norm
}

global.splines <- function(madat,cv=FALSE,flaglevel=-200) {
 madat$intensities <- subset(madat$intensities,flags > flaglevel)
 norm <- madat

 smoothed <- smooth.spline(madat$intensities$a,madat$intensities$m)
 c <- predict(smoothed,madat$intensities$a)$y
 norm$intensities$m <- norm$intensities$m - c
 norm$norm <- 'Splines Global'
 norm
}

subarray.splines <- function(madat,cv=FALSE,flaglevel=-200) {
 madat$intensities <- subset(madat$intensities,flags > flaglevel)
 norm <- madat
 
 for(i in 1:max(madat$intensities$block)) {
      subarray <- subset(madat$intensities,block == i,select=c(m,a))
      smoothed <- smooth.spline(subarray$a,subarray$m,cv=cv)
      c <- predict(smoothed,subarray$a)$y 
      norm$intensities$m[norm$intensities$block == i] <- norm$intensities$m[norm$intensities$block == i] - c
 }
 
 norm$norm <- 'Splines por Subarray'
 norm
}

subarray.norm <- function(madat,flaglevel=-200) {
 madat$intensities <- subset(madat$intensities,flags > flaglevel)
 norm <- madat

  for(i in 1:max(madat$intensities$block)) {
    c <- mean(madat$intensities$m[madat$intensities$block == i])
    norm$intensities$m[norm$intensities$block == i] <- norm$intensities$m[norm$intensities$block == i] - c
   }

  norm$norm <- 'Intensidade Total por Subarray'
  norm

}

global.norm <- function(madat,flaglevel=-200) {
  madat$intensities <- subset(madat$intensities,flags > flaglevel)
  norm <- madat

  c <- mean(madat$intensities$m)
  norm$intensities$m <- madat$intensities$m - c
  norm$norm <- 'Intensidade Total Global'
  norm
}