Dear Sophie you could have a look at Section 7 "Normalisation with ?spike-in? probes" of the vsn package vignette. Best wishes Wolfgang Jul/3/12 11:35 AM, Sophie Lamarre scripsit:: > Hi Jim, > > Now I understand the problem! > But I have to normalize diagnostic microarray so I'm looking for several > methods of normalization in order to retain the best. I can't use the > quantile normalization because I don't know if the majority of genes are > invariants. > I think the housekeeping genes normalization could be a possible > normalization. I selected the 20 housekeeping genes which seem to be the > least invariants. > I don't think the normalization with the invariantset function is > appropriated in my case. > > But if you have any suggestions, I would be glad! > > Thank you very much for your help, > > Sophie > > Le 02/07/2012 18:31, James W. MacDonald a ?crit : >> Hi Sophie, >> >> On 7/2/2012 10:35 AM, Sophie Lamarre wrote: >>> Hello Jim, >>> >>> I have 151 patients in my file and 16 417 genes without the 20 >>> housekeeping genes I need to normalize. >>> I want to try different normalization methods using housekeeping genes. >>> The classic method is to calculate the mean of the housekeeping genes >>> (selected) by patient, and subtract this value to each genes of the >>> same patient. >>> >>> I would try the invariant set method with my data file and my list of >>> housekeeping genes. >>> When I read the help, one said I had to have 2 vectors: my data file >>> to normalize and my file containing the intensities of housekeeping >>> genes (which help me to normalize): >> >> Ah, I see. The problem here is that you misunderstand what >> normalize.invariantset() is intended to do. It is not intended to do >> what you want, which is to use a set of housekeeping genes to >> normalize the data. Instead, this is really an internal function for >> normalize.AffyBatch.invariantset(). >> >> The idea here is to take one chip (which is what you did), and then >> some artificially derived 'reference' chip that contains the same >> number of genes as your chip (and is derived from the mean, median, >> etc for each gene), and then determine which genes don't change >> expression between the two, and then fit a line on those 'invariant' >> genes, which will then be used to normalize your data. If your two >> vectors are not the same length, you will get the error you see. >> >> This is quite different from what you want to do. I don't think there >> are any functions to do such a simple normalization, and quite frankly >> what you propose is neither classic nor recommended (if by classic you >> mean 'a very common and accepted method' rather than 'what people did >> way back in the past before they knew better'). >> >> To do what you propose is just a simple application of colMeans() and >> sweep(). >> >> Best, >> >> Jim >> >> >>> >>> Usage >>> >>> normalize.AffyBatch.invariantset(abatch, prd.td = c(0.003, 0.007), >>> verbose = FALSE, >>> baseline.type = >>> c("mean","median","pseudo-mean","pseudo-median"), >>> type = >>> c("separate","pmonly","mmonly","together")) >>> >>> normalize.invariantset(data, ref, prd.td=c(0.003,0.007)) >>> >>> >>> Arguments >>> >>> |abatch| >>> >>> an|AffyBatch <affybatch%2dclass.html>|object. >>> >>> |data| >>> >>> a vector of intensities on a chip (to normalize to the reference). >>> >>> |ref| >>> >>> a vector of reference intensities. >>> >>> >>> >>> Thank you for your help, >>> >>> Kind Regards, >>> -- >>> Sophie LAMARRE >>> >>> >>> Le 02/07/2012 16:12, James W. MacDonald a ?crit : >>>> Hi Sophie, >>>> >>>> On 7/2/2012 8:03 AM, Sophie Lamarre wrote: >>>>> Hello, >>>>> >>>>> I try the invariantset normalization function (affy package) on my >>>>> data: >>>>> >>>>>> test_pat1 = >>>>>> normalize.invariantset(data_ready_to_normalize_met1[,1], >>>>> + bd_20hk_norm[,1], >>>>> + prd.td=c(0.003,0.007)) >>>>> Error on while ((ns.old - ns)> 50) { : >>>>> missing value where TRUE / FALSE is required >>>> >>>> When you do >>>> >>>> data_ready_to_normalize_met1[,1] >>>> >>>> >>>> you are selecting data from only one array. It isn't possible to >>>> figure out which probesets are invariant with only one array >>>> (because the implication is that the probesets don't vary in any >>>> array). >>>> >>>> Is there a particular reason that you are trying to normalize just >>>> one array? >>>> >>>> Best, >>>> >>>> Jim >>>> >>>> >>>> >>>>> >>>>> >>>>> # My data to normalize >>>>> >>>>>> data_ready_to_normalize_met1[1:5,1] >>>>> [1] 5.803779 11.566477 8.583049 8.531674 9.490483 >>>>> >>>>> # My vector containing my 20 housekeeping genes >>>>>> bd_20hk_norm[1:5,1] >>>>> [1] 14.92680 15.58281 15.15885 15.09599 15.23146 >>>>> >>>>> My session info: >>>>> >>>>> >>>>>> sessionInfo() >>>>> R version 2.14.1 (2011-12-22) >>>>> Platform: x86_64-redhat-linux-gnu (64-bit) >>>>> >>>>> locale: >>>>> [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C >>>>> LC_TIME=fr_FR.UTF-8 >>>>> [4] LC_COLLATE=fr_FR.UTF-8 LC_MONETARY=fr_FR.UTF-8 >>>>> LC_MESSAGES=fr_FR.UTF-8 >>>>> [7] LC_PAPER=C LC_NAME=C >>>>> LC_ADDRESS=C >>>>> [10] LC_TELEPHONE=C LC_MEASUREMENT=fr_FR.UTF-8 >>>>> LC_IDENTIFICATION=C >>>>> >>>>> attached base packages: >>>>> [1] grid stats graphics grDevices utils datasets >>>>> methods base >>>>> >>>>> other attached packages: >>>>> [1] affy_1.32.1 preprocessCore_1.16.0 >>>>> gplots_2.10.1 KernSmooth_2.23-7 >>>>> [5] caTools_1.13 bitops_1.0-4.1 >>>>> gdata_2.8.2 gtools_2.6.2 >>>>> [9] geneplotter_1.32.1 lattice_0.20-0 >>>>> annotate_1.32.3 AnnotationDbi_1.16.19 >>>>> [13] Biobase_2.14.0 limma_3.10.3 >>>>> >>>>> loaded via a namespace (and not attached): >>>>> [1] affyio_1.22.0 BiocInstaller_1.2.1 DBI_0.2-5 >>>>> IRanges_1.12.6 >>>>> [5] RColorBrewer_1.0-5 RSQLite_0.11.1 tools_2.14.1 >>>>> xtable_1.7-0 >>>>> [9] zlibbioc_1.0.1 >>>>> >>>>> >>>>> I have no missing value: >>>>> >>>>>> test = is.na(data_ready_to_normalize_met1[,1]) >>>>>> sum(test) >>>>> [1] 0 >>>>> >>>>> >>>>> >>>>> Could you help me or give me a example in order I can resolve my >>>>> problem? >>>>> >>>>> Thank your very much, >>>>> >>>>> Kind Regards, >>>>> >>>>> Sophie LAMARRE >>>>> >>>>> [[alternative HTML version deleted]] >>>>> >>>>> _______________________________________________ >>>>> Bioconductor mailing list >>>>> Bioconductor at r-project.org >>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>> Search the archives: >>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>> >> > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Best wishes Wolfgang Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber