FYI, the aroma.affymetrix package can reproduce the gcrma() estimates with great precision, cf. http://www.aroma-project.org/replication/gcRMA It does this by utilizing the gcrma functions while running in bounded memory (~1 GB RAM) regardless of the number of arrays. /Henrik On Thu, Feb 4, 2010 at 2:56 PM, James W. MacDonald <jmacdon at="" med.umich.edu=""> wrote: > Hi Jenny, > > It wasn't posted to the list because Jin got tired of waiting for a response > from the list and emailed me directly, so the correspondence was just > between the two of us. > > It was my oversight to keep the conversation off-list. Below is the > transcript of our conversation, for the archives (and apologies for keeping > this 'secret'). > > Hi Jin, > > I just checked in a modification to justGCRMA that results in identical > output as compared to gcrma. There have been quite a few modifications to > gcrma since I originally wrote justGCRMA, and it is no longer possible for > justGCRMA to do everything that one can do with gcrma. > > Specifically, in the past gcrma used the MM probes on a chip to estimate > background, but this has been changed so the default is to use internal > estimates that come with the package. The functionality to use MM probes (or > a subset thereof) still exists, but it is too much work to modify justGCRMA > to use the MM probes, not to mention the headache of trying to maintain the > code. So justGCRMA will only use the internal NSB estimates that come with > the package, and will give identical results to gcrma as long as you use the > internal NSB estimates for both functions. > > The new version of gcrma is 2.18.1, and should be available for installation > within the next couple of days. > > Best, > > Jim > > >>>> >>> Jin <jinxing1986 at="" gmail.com=""> wrote: >> > Hello Dr. MacDonald, >> > >> > I was wondering if you could help me with a problem. I've ran my >> > dataset through GCRMA and justGCRMA but I've gotten different >> > numerical results from both. I was under the impression that these two >> > functions were identical except justGCRMA was more memory efficient >> > because it did not have to create an AffyBatch object. I used the >> > default parameters for both meaning I did not include any parameters >> > at all when calling gcrma() and justgcrma(). >> > >> > Are they suppose to give identical numerical results? >> > >> > For example, below are some sample results. The first row is GCRMA and >> > the second row is justGCRMA. The columns are my 8 arrays. JustGCRMA >> > values are consistently higher than GCRMA values whereas I would have >> > expected similar numbers matching the top and bottom rows. >> > >> > 3.58 ? ? ? ?3.80 ? ?3.83 ? ?4.52 ? ?3.91 ? ?5.17 ? ?3.78 ? ?6.45 >> > 4.79 ? ? ? ?4.73 ? ?5.21 ? ?5.68 ? ?5.44 ? ?5.88 ? ?5.11 ? ?7.76 >> > >> > >> > >> > Thanks, >> > Jin >> > University of California, San Diego > > > Jenny Drnevich wrote: >> >> Thanks Jim, >> >> Thanks for the info. I found the question that was posted last last year >> (and responded to it to start the thread again), but I still can't find your >> response on BioC. Perhaps it accidently didn't get sent back to the list? >> >> :) >> Jenny >> >> At 04:31 PM 2/3/2010, James W. MacDonald wrote: >>> >>> Hi Jenny, >>> >>> Jenny Drnevich wrote: >>>> >>>> Hi everyone, >>>> I just spent several hours tracking down this problem, as I noticed this >>>> same discrepancy between the results of justGCRMA() and gcrma() called on >>>> the same data. There was also another post about this on Nov 4, 2008, but I >>>> couldn't find where either of them had ever been answered. >>> >>> I think I missed that one, but the question was asked again and answered >>> by me late last year. >>> >>>> HOWEVER, it appears that the discrepancy has been fixed in R 2.10.1 >>>> (gcrma 2.18.1), but it was in R 2.10.0 (gcrma 2.18.0) (examples and >>>> sessionInfos below). >>>> While I'm glad it has been "fixed", where would this have been >>>> documented? I didn't think this type of change would have been included in a >>>> minor version upgrade. What was the explanation for the original >>>> discrepancy? >>> >>> The explanation is that justGCRMA() is a function that I wrote several >>> years ago. In the intervening period the defaults for gcrma() were changed >>> without making the corresponding changes to justGCRMA(). >>> >>> I made the (unfounded) assumption that the maintainer of the gcrma >>> package (Jean Wu) was keeping justGCRMA() consistent with gcrma(), which as >>> you have seen is not true. Since this was an oversight by both me and Jean, >>> it wasn't documented anywhere. >>> >>> I have since made the two functions consistent when running under the >>> default arguments. However, there are some arguments for gcrma() that are >>> not available for justGCRMA(), so they are not consistent in all cases. >>> >>> Best, >>> >>> Jim >>> >>> >>>> Thanks, >>>> Jenny >>>> #The examples below are not reproducible because you need .CEL files - >>>> run them on any that you have. >>>> ?> library(gcrma) >>>> Loading required package: affy >>>> Loading required package: Biobase >>>> Welcome to Bioconductor >>>> ?Vignettes contain introductory material. To view, type >>>> ?'openVignette()'. To cite Bioconductor, see >>>> ?'citation("Biobase")' and for packages 'citation(pkgname)'. >>>> ?> setwd("K:/Bulla/CELfiles") >>>> ?> raw <- ReadAffy() >>>> ?> raw >>>> AffyBatch object >>>> size of arrays=834x834 features (10 kb) >>>> cdf=Rat230_2 (31099 affyids) >>>> number of samples=6 >>>> number of genes=31099 >>>> annotation=rat2302 >>>> notes= >>>> ?> >>>> ?> gcrma.1 <- gcrma(raw) >>>> Adjusting for optical effect......Done. >>>> Computing affinitiesLoading required package: AnnotationDbi >>>> .Done. >>>> Adjusting for non-specific binding......Done. >>>> Normalizing >>>> Calculating Expression >>>> ?> >>>> ?> gcrma.2 <- justGCRMA() >>>> Computing affinities.Done. >>>> Adjusting for optical effect.......Done. >>>> Adjusting for non-specific binding......Done. >>>> Normalizing >>>> Calculating Expression >>>> ?> >>>> ?> all.equal(exprs(gcrma.1),exprs(gcrma.2)) >>>> [1] "Mean relative difference: 0.03514035" >>>> ?> >>>> ?> sessionInfo() >>>> R version 2.10.0 (2009-10-26) >>>> i386-pc-mingw32 >>>> locale: >>>> [1] LC_COLLATE=English_United States.1252 ?LC_CTYPE=English_United >>>> States.1252 >>>> [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C >>>> [5] LC_TIME=English_United States.1252 >>>> attached base packages: >>>> [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base >>>> other attached packages: >>>> [1] rat2302probe_2.5.0 ?AnnotationDbi_1.8.1 rat2302cdf_2.5.0 >>>> gcrma_2.18.0 >>>> [5] affy_1.24.2 ? ? ? ? Biobase_2.6.0 >>>> loaded via a namespace (and not attached): >>>> [1] affyio_1.14.0 ? ? ? ?Biostrings_2.14.3 DBI_0.2-4 >>>> IRanges_1.4.4 >>>> [5] preprocessCore_1.8.0 RSQLite_0.7-3 ? ? ? ?splines_2.10.0 >>>> tools_2.10.0 >>>> ?> >>>> #Now switch R versions but run same code.... >>>> ?> library(gcrma) >>>> Loading required package: affy >>>> Loading required package: Biobase >>>> Welcome to Bioconductor >>>> ?Vignettes contain introductory material. To view, type >>>> ?'openVignette()'. To cite Bioconductor, see >>>> ?'citation("Biobase")' and for packages 'citation(pkgname)'. >>>> ?> setwd("K:/Bulla/CELfiles") >>>> ?> raw <- ReadAffy() >>>> ?> raw >>>> AffyBatch object >>>> size of arrays=834x834 features (10 kb) >>>> cdf=Rat230_2 (31099 affyids) >>>> number of samples=6 >>>> number of genes=31099 >>>> annotation=rat2302 >>>> notes= >>>> ?> >>>> ?> gcrma.1 <- gcrma(raw) >>>> Adjusting for optical effect......Done. >>>> Computing affinitiesLoading required package: AnnotationDbi >>>> .Done. >>>> Adjusting for non-specific binding......Done. >>>> Normalizing >>>> Calculating Expression >>>> ?> >>>> ?> gcrma.2 <- justGCRMA() >>>> Computing affinities.Done. >>>> Adjusting for optical effect.......Done. >>>> Adjusting for non-specific binding......Done. >>>> Normalizing >>>> Calculating Expression >>>> ?> >>>> ?> all.equal(exprs(gcrma.1),exprs(gcrma.2)) >>>> [1] TRUE >>>> ?> >>>> ?> sessionInfo() >>>> R version 2.10.1 (2009-12-14) >>>> i386-pc-mingw32 >>>> locale: >>>> [1] LC_COLLATE=English_United States.1252 ?LC_CTYPE=English_United >>>> States.1252 >>>> [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C >>>> [5] LC_TIME=English_United States.1252 >>>> attached base packages: >>>> [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base >>>> other attached packages: >>>> [1] rat2302probe_2.5.0 ?AnnotationDbi_1.8.1 rat2302cdf_2.5.0 >>>> gcrma_2.18.1 >>>> [5] affy_1.24.2 ? ? ? ? Biobase_2.6.1 >>>> loaded via a namespace (and not attached): >>>> [1] affyio_1.14.0 ? ? ? ?Biostrings_2.14.10 DBI_0.2-5 >>>> IRanges_1.4.9 >>>> [5] preprocessCore_1.8.0 RSQLite_0.8-0 ? ? ? ?splines_2.10.1 >>>> tools_2.10.1 >>>> ?> >>>> >>>> >>>> At 04:31 PM 10/28/2009, Jin wrote: >>>>> >>>>> Jerry <norn2k at="" ...=""> writes: > > Hi, > ? ?> I'm currently using gcrma >>>>> package in R to summarize probeset intensities from CEL files. Surprisingly, >>>>> > I? found that the results generated from gcrma function and justGCRMA >>>>> function are quite different. In > general,? expression values from gcrma >>>>> function? are lower and? the boxplots from gcrma are? quite > asymmetric (no >>>>> tails? on the bottom).? I attached some plots below for your information. >>>>> This confused > me as I thought that the two functions implemented similar >>>>> algorithms.? ?> ? ?> Thank you so much for your help! > ? ?> Best, > Jianjun >>>>> > > PS: The package I used is gcrma 2.12.1. I also observed similar results >>>>> on? 2.14 version. > > ? ? ? Hello, I'm running into the same problem with >>>>> gcrma and justGCRMA. They are not giving the same numeric results. Is this >>>>> an intended feature? I was under the impression that these two methods were >>>>> identical with the exception that justGCRMA was more memory efficient >>>>> because it didn't have to create an AffyBatch object. Can anyone shed some >>>>> light on this subject? Thanks, Jin >>>>> _______________________________________________ ?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 >>>> >>>> Jenny Drnevich, Ph.D. >>>> Functional Genomics Bioinformatics Specialist >>>> W.M. Keck Center for Comparative and Functional Genomics >>>> Roy J. Carver Biotechnology Center >>>> University of Illinois, Urbana-Champaign >>>> 330 ERML >>>> 1201 W. Gregory Dr. >>>> Urbana, IL 61801 >>>> USA >>>> ph: 217-244-7355 >>>> fax: 217-265-5066 >>>> e-mail: drnevich at illinois.edu >>> >>> -- >>> 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 >> >> Jenny Drnevich, Ph.D. >> >> Functional Genomics Bioinformatics Specialist >> W.M. Keck Center for Comparative and Functional Genomics >> Roy J. Carver Biotechnology Center >> University of Illinois, Urbana-Champaign >> >> 330 ERML >> 1201 W. Gregory Dr. >> Urbana, IL 61801 >> USA >> >> ph: 217-244-7355 >> fax: 217-265-5066 >> e-mail: drnevich at illinois.edu > > -- > 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 > _______________________________________________ > 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 >