Thanks James!!!! The paper that I refered was a recent one 2010 so I thought was easier to follow. I think as you said it might be better to choose an another method. On Fri, Dec 17, 2010 at 11:51 AM, James W. MacDonald <jmacdon@med.umich.edu>wrote: > Hi Viritha, > > > On 12/17/2010 11:11 AM, viritha kaza wrote: > >> Hi James, >> I am actually interested in getting a raw (unnormalised) microarray >> expression dataset. Since I am interested in performing this for many >> datasets, I would like to perform normalization as one of the paper >> suggests >> to remove bias due to the sample preparation and different platforms- >> "Briefly, for each expression data set, individual probe intensity of each >> array was divided by the averaged probe intensity across all arrays within >> the data set, then each value was log (base 2) transformed. For >> normalization, first, average expression value of all probes in each array >> was calculated. Then for each array, expression value of each probe was >> subtracted by the averaged expression value. By doing so, average >> expression >> value of all probes in each array in each expression data set will be >> zero." >> > > Two things here: > > 1.) That normalization is as naive as you can possibly get. We have gone > _way_ past the stage where people think a simple location normalization is a > reasonable thing to do. > > All this does is shift the data so the means line up, not taking into > account that there might be more subtle technical artifacts that should be > removed. You will be much better served by using the stock normalization in > rma(), or if you really want to get fancy, you might want to use vsn. But > you will be regressing to maybe the year 2000 if you use the normalization > you suggest here. > > 2.) The normalization you are considering is designed for spotted arrays, > where each spot measures transcript from two different samples. Because of > that fact, the data are usually reported as a ratio (e.g., cy3/cy5). For > these data, exact equivalence of transcript would be expected to be a 1 > (e.g., equal amounts of cy3 and cy5 fluorescence). If you then take logs, > equivalence will then be equal to zero. > > In that case, taking the mean and subtracting (centering on the mean) is a > reasonable but naive thing to do. However, in your case, the data range from > approximately 2^6 - 2^14 or so. If you take log_2 of these data, they will > then range from 6 - 14. Because they aren't ratios, and they aren't really > symmetrically distributed there isn't a compelling reason to normalize to > zero. > > If you still want to progress with this idea, note that pretty much all of > the summarization methods have a normalize argument, so you can simply set > normalize = FALSE, and you will then get unnormalized, summarized data. > > See e.g., ?rma > > Best, > > Jim > > > > Hence to perform above steps I thought I would need a raw expression >> dataset >> from the cell files afterwhich I can normalise by the above strategy to >> remove bias.So I am expecting to get a single value for each probe in an >> array. >> I hope this helps in understanding what exactly I want the expression >> dataset to be. >> Thanks, >> Viritha >> >> On Fri, Dec 17, 2010 at 10:00 AM, James W. MacDonald >> <jmacdon@med.umich.edu>wrote: >> >> >>> >>> On 12/16/2010 3:35 PM, viritha kaza wrote: >>> >>> Thanks James.There was no error. >>>> But I see that I get 11 values for the same probe.Why does it happen? If >>>> I >>>> perform MM as well then again I would get another file.How do I finally >>>> get >>>> one value for each probe in an array? >>>> >>>> >>> I think we need to back up a bit here. On Affy chips there are multiple >>> probes used to interrogate a single transcript. As you note, for this >>> particular chip there are usually 11 probes. All of the probes for a >>> given >>> transcript make up a probeset. >>> >>> When we process these data, we first background correct and normalize the >>> probe values to eliminate as much non-biological variability as possible, >>> and then we summarize all the probes in each probeset to generate the >>> final >>> value, which we hope is proportional to the expression of the transcript >>> we >>> are trying to measure. >>> >>> So we have to be precise about our terminology. You originally asked for >>> a >>> text file containing unnormalized probe values, which is what the code I >>> supplied does. Evidently that is not what you wanted, so can you >>> precisely >>> state what it is that you do want? >>> >>> Best, >>> >>> Jim >>> >>> >>> >>> >>> >>> Thanks, >>> >>>> Viritha >>>> >>>> On Thu, Dec 16, 2010 at 2:18 PM, James W. MacDonald >>>> <jmacdon@med.umich.edu>wrote: >>>> >>>> Make that >>>> >>>>> >>>>> fun<- function(q,r){ >>>>> row.names(r)<- rep(q, nrow(r)) >>>>> r >>>>> } >>>>> >>>>> Which of course makes more sense. >>>>> >>>>> Jim >>>>> >>>>> >>>>> >>>>> >>>>> On 12/16/2010 12:04 PM, viritha kaza wrote: >>>>> >>>>> Hi James, >>>>> >>>>>> Thanks for your reply, >>>>>> I am new to R statistics. >>>>>> Do I have to give the values for q or r because I am getting the >>>>>> following >>>>>> error when I type mapply command - >>>>>> >>>>>> Error in dimnames(x)<- dn : >>>>>> length of 'dimnames' [1] not equal to array extent >>>>>> >>>>>> There are 5 arrays in the experiment. >>>>>> >>>>>> Thank you, >>>>>> Viritha >>>>>> >>>>>> >>>>>> On Thu, Dec 16, 2010 at 11:22 AM, James W. MacDonald >>>>>> <jmacdon@med.umich.edu>wrote: >>>>>> >>>>>> Hi Viritha, >>>>>> >>>>>> >>>>>>> >>>>>>> On 12/16/2010 10:45 AM, viritha kaza wrote: >>>>>>> >>>>>>> Hi Group, >>>>>>> >>>>>>> Let me clearly explain.I have the [Mouse430_2] Affymetrix Mouse >>>>>>>> Genome >>>>>>>> 430 >>>>>>>> 2.0 Array.I want to create an unnormalised expression microarray >>>>>>>> data >>>>>>>> set.I >>>>>>>> have the cell files and cdf file for this.I want the intensities in >>>>>>>> the >>>>>>>> probe level.Is this possible in R or any other source? or how can I >>>>>>>> get >>>>>>>> this >>>>>>>> expression microarray dataset? >>>>>>>> >>>>>>>> >>>>>>>> library(affy) >>>>>>>> >>>>>>> dat<- ReadAffy() >>>>>>> pms<- pm(dat, LISTRUE=TRUE) >>>>>>> fun<- function(q,r){ >>>>>>> row.names(r)<- rep(q, ncol(r)) >>>>>>> r >>>>>>> } >>>>>>> >>>>>>> pms<- mapply(fun, names(pms), pms, SIMPLIFY = FALSE) >>>>>>> pms<- do.call("rbind", pms) >>>>>>> write.table(pms, "Raw PM data.txt", quote = FALSE, row.names = TRUE, >>>>>>> col.names = TRUE, sep = "\t") >>>>>>> >>>>>>> You can do similar for MM probes if you desire. >>>>>>> >>>>>>> Best, >>>>>>> >>>>>>> Jim >>>>>>> >>>>>>> >>>>>>> >>>>>>> Thank you in advance, >>>>>>> >>>>>>> Viritha >>>>>>> >>>>>>>> >>>>>>>> On Wed, Dec 15, 2010 at 4:05 PM, viritha kaza<viritha.k@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>> Hi group, >>>>>>>> >>>>>>>> If I want to create raw txt file of microarray data from the >>>>>>>> >>>>>>>>> (affymetrix) >>>>>>>>> cell file, how do I create the expression set with raw signal >>>>>>>>> intensity.I >>>>>>>>> know that only cell file with the version 3 can be opened as excel >>>>>>>>> file >>>>>>>>> as >>>>>>>>> it is in ascii format. >>>>>>>>> In one such cell file the intensity is indicated as: >>>>>>>>> CellHeader=X Y MEAN STDV NPIXELS 0 0 137.3 25.1 36 1 0 10730.5 >>>>>>>>> 2009.9 >>>>>>>>> 36 2 0 136.3 21.2 36 >>>>>>>>> But I am not sure how to assign the probe numbers to the >>>>>>>>> CellHeaders and I would also like to know if the raw intensity >>>>>>>>> taken >>>>>>>>> is >>>>>>>>> just >>>>>>>>> the mean intensity? Can this be performed in R? >>>>>>>>> Waiting for your response, >>>>>>>>> Thank you in advance, >>>>>>>>> Viritha >>>>>>>>> >>>>>>>>> >>>>>>>>> [[alternative HTML version deleted]] >>>>>>>>> >>>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> 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 >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> >>>>>>> James W. MacDonald, M.S. >>>>>>> Biostatistician >>>>>>> Douglas Lab >>>>>>> University of Michigan >>>>>>> Department of Human Genetics >>>>>>> 5912 Buhl >>>>>>> 1241 E. Catherine St. >>>>>>> Ann Arbor MI 48109-5618 >>>>>>> 734-615-7826 >>>>>>> ********************************************************** >>>>>>> Electronic Mail is not secure, may not be read every day, and should >>>>>>> not >>>>>>> be >>>>>>> used for urgent or sensitive issues >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> >>>>>> James W. MacDonald, M.S. >>>>> Biostatistician >>>>> Douglas Lab >>>>> University of Michigan >>>>> Department of Human Genetics >>>>> 5912 Buhl >>>>> 1241 E. Catherine St. >>>>> Ann Arbor MI 48109-5618 >>>>> 734-615-7826 >>>>> ********************************************************** >>>>> Electronic Mail is not secure, may not be read every day, and should >>>>> not >>>>> be >>>>> used for urgent or sensitive issues >>>>> >>>>> >>>>> >>>> -- >>> James W. MacDonald, M.S. >>> Biostatistician >>> Douglas Lab >>> University of Michigan >>> Department of Human Genetics >>> 5912 Buhl >>> 1241 E. Catherine St. >>> Ann Arbor MI 48109-5618 >>> 734-615-7826 >>> ********************************************************** >>> Electronic Mail is not secure, may not be read every day, and should not >>> be >>> used for urgent or sensitive issues >>> >>> > -- > James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be > used for urgent or sensitive issues > [[alternative HTML version deleted]]