Incidentally, this would be a really dumb idea (qnorm'ing read counts) if you wanted to do sensitive differential (expression/DNAse/ChIP/whatever) because it destroys count information. But that's another matter. On Tue, Jun 4, 2013 at 9:46 AM, Tim Triche, Jr. <tim.triche@gmail.com>wrote: > if you have different numbers of peaks, bins, whatever, you will need to > do kernel smoothing to get the appropriate distribution of quantiles, then > normalize to that. Pan Du implemented this in the 'lumi' package, FWIW. > Personally I think it's a clever enough idea (kernel-smoothed quantile > normalization) that you should read it anyways, but... > > If you just want to normalize equal numbers of bins, it is pretty > straightforward, e.g. suppose you have 100 runs of 100 bins/peaks/whatever > (key point being they all have the same number of things per run to > normalize ranks of): > > library(preprocessCore) > > par(mfrow=c(1,2)) > foo <- matrix(rpois(10000, rgamma(1.5, 90)), ncol=100) > plot(density(foo[,1]), xlab='reads', main='before') > for(i in 2:100) lines(density(foo[,i])) > > bar <- normalize.quantiles(foo) > plot(density(bar[,1]), xlab='reads', main='after') > for(i in 2:100) lines(density(bar[,i])) > > > sessionInfo() > ## R version 3.0.0 (2013-04-03) > ## Platform: x86_64-unknown-linux-gnu (64-bit) > ## > ## attached base packages: > ## [1] stats graphics grDevices datasets utils methods base > > ## > ## other attached packages: > ## [1] preprocessCore_1.23.0 BiocInstaller_1.11.1 gtools_2.7.1 > ## [4] devtools_1.2 > ## > ## loaded via a namespace (and not attached): > ## [1] digest_0.6.3 evaluate_0.4.3 httr_0.2 memoise_0.1 > parallel_3.0.0 > ## [6] RCurl_1.95-4.1 stringr_0.6.2 tools_3.0.0 whisker_0.3-2 > > > > On Tue, Jun 4, 2013 at 6:21 AM, Yashwant Kumar [guest] < > guest@bioconductor.org> wrote: > >> >> I have to run preprocess core for qantile normalization of my data. Data >> has protein name in column and peak intensity in row. >> >> I have data of different time point. each time point has three >> replicates. I did all protein normalization for now but two time points are >> not behaving as expected. So I would like to do quantile normalization >> using for the data and this I don't want between replicates but between >> time points. >> >> Please advise me how to do this. I can run R if you will explain me to >> matrix preparation. >> >> Best regards, >> Yashwant >> >> -- output of sessionInfo(): >> >> no >> >> -- >> Sent via the guest posting facility at bioconductor.org. >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > > > -- > *A model is a lie that helps you see the truth.* > * > * > Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> > -- *A model is a lie that helps you see the truth.* * * Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> [[alternative HTML version deleted]]