Hi, one option is to use the normalization methods in 'aroma.light', which take matrices (or lists) containing signals on the intensity scale (non-logged signals), so you could do: library(aroma.light) X <- as.matrix(df) and pick one of: Xn <- normalizeAverage(X) Xn <- normalizeQuantile(X) Xn <- normalizeCurveFit(X) followed by dfN <- as.data.frame(Xn) For normalizeCurveFit() you can specify either method="loess" (default/slowest), "lowess", "spline", or "robustSpline". See help(aroma.light) for more details and help(normalizeCurveFit.matrix) for an example. Hope this helps /Henrik PS. I'll add it to the to do list for the next release to add normalization wrappers for data.frame:s as well so you don't have to do the above coercions. DS. On Feb 11, 2008 11:26 PM, joseph <jdsandjd at="" yahoo.com=""> wrote: > > > Hi > > I have a data frame that looks like the one below. I would like to apply to its columns the different > kinds of normalizations such as median global normalization (Scaling), Quantile > Normalization, and Lowess. Do I have to use packages such as affy, Limma and > marray and if so what do I have to do to the data frame in order to be able to > analyze it with these packages? > > > Thanks > > > > > > df=data.frame (row.names=LETTERS[1:10], col1=c(1,4,7:9,2:6), > col2= c(1:4,9, 10:14), col3=c(14,6:10,8:11)) > > > > df > > > col1 col2 col3 > > > A 1 1 > 14 > > > B 4 2 > 6 > > > C 7 3 > 7 > > > D 8 4 > 8 > > > E 9 9 > 9 > > > F 2 10 > 10 > > > G 3 11 > 8 > > > H 4 > 12 9 > > > I 5 13 > 10 > > > J 6 14 > 11 > > > > > > > > > > > > > ______________________________________________________________ ______________________ > Looking for last minute shopping deals? > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >