limma allows you to normalize on any set of control spots using any of the normalization methods "loess", "printtiploess", "robustspline". See page 15 of the User's Guide. (Although I don't actually recommend this with spike-in controls, because of the risk of systematic bias in spike-in volumes relative to sample volumes.) The technique is simply to set up a vector or matrix 'w' which is 1 for controls that you want to use for normalization and 0 otherwise. For example, MA <- normalizeWithinArrays(RG, method="loess", weights=w) will put a loess curve through the control spots and will subtract this curve from all the other spots. You have asked to subtract the mean log-ratio for the spike-in from the other spots. This is not specifically provided for in limma (I don't recommend it), but it is easy. Suppose 'isSpikeIn' is a vector of TRUE/FALSE indicating which probes are spike-in controls. Then MA <- normalizeWithinArrays(RG, method="none") m <- colMeans(MA$M[isSpikeIn,],na.rm=TRUE) MA$M <- MA$M - ( matrix(1,nrow(MA),1) %*% m ) Gordon >[BioC] spike control normalization >NATALIA F TCHETCHERINA nxt7 at psu.edu >Tue Jun 28 19:30:49 CEST 2005 > >Hello all, >I have experiment where a large fraction of genes are expected to be >differentially expressed. That why I would like to normalize the data using >spike control spots (I really would like to do within arrays normalization >just >subtract over the whole array ( or witnin i-th block) mean of log- ratio of >spike control from log-ratio for each gene). I use Limma. >My question is: does limma have options for such normalization? >If it does not that package I should use? > >Sincerely, Natalia.