Xps package : errors and RMA difference with Partek GS
1
0
Entering edit mode
@arnaud-le-cavorzin-3364
Last seen 7.1 years ago
Hi. Thank you for your anwser. I have downloaded the last version of xps and performed the metaProbesets with success. So if I understand, xps use the same background correction algorithm than Partek or APT. But the difference between these softwares seems to be caused by the probes used for the background correction. Moreover it seems to get a difference not only for the background correction, but for the quantile normalization too, again due to the different probes. In fact I can't get the same results on my samples with xps package and with Partek. With Partek we found no expression differences for all of the genes (after fdr correction), while xps found a little more than 1100 genes who are differentially expressed (after using prefilter() and unifilter() function, with the same options than in Partek). And I don't know why. Thank you for your help and your answer, Best regards Arnaud > Date: Tue, 31 Mar 2009 22:04:42 +0200 > From: cstrato@aon.at > To: arnaudlc@msn.com > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu-rennes.fr > Subject: Re: [BioC] Xps package : errors and RMA difference with Partek GS > > Dear Arnaud, > > Let me first answer your question how to export the background data > (although an example is shown in vignette xps.pdf p.15): > > # 1. compute background (or rma) > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", filedir=datdir, > method="rma", select="antigenomic", option="pmonly:epanechnikov", > params=c(16384), exonlevel="core") > > # 2. find background treenames for data.bg > getTreeNames(rootFiledata.bg)) > > # 3. get background for all trees > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > "*", "rbg", "fBg", "BgrdAll.txt") > # or get background intensity for e.g. tree "BreastA.rbg" > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > In addition you can also export the background subtracted intensities: > # 4. get background corrected intensities for all trees > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > "*", "int", "fInten", "IntenAll.txt") > > However, please note that only the "core" probes are corrected, so I am > not sure if you can use these data in another program. > > > Some notes on background correction: > > Please note that the above background correction does NOT use > "antigenomic" probes for background correction. The parameter > select="antigenomic" does only define the kind of MM probes (although > they are not used in this case). Which probes are used as PM probes is > defined by exonlevel="core", which means that only "core" probes are > used for background correction. > > As you know, in the RMA background algorithm observed PM probes are > modeled as the sum of a normal noise component and an exponential signal > component. Since in above case only "core" probes are selected as PM > probes, only these probes are used for background correction. This may > be the reason why the background data differ from the background data > computed by APT, as explained in vignette APTvsXPS.pdf. > > If you want to compute the background using "genomic" or "antigenomic" > probes and the APT algorithm based on GC content of these probes then > you need to use: > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", filedir=datdir, > method="gccontent", select="antigenomic", option="attenuatebg", > params=c(0.4, 0.005, -1.0), exonlevel="core") > or the dedicated function: > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", filedir=datdir, > select="antigenomic", exonlevel="core") > > > Maybe one note on processing time: > My main goal was to allow processing of exon arrays on computers with > 1GB RAM only, and to allow access to all interim data such as background > intensities and background-corrected probe intensities. Thus, all these > data are stored as root trees, which means that saving all these interim > data on HD is probably the time-consuming step. > > I hope that I could answer your questions. > > Best regards > Christian > > > arnaud Le Cavorzin wrote: > > Hi > > > > Thank you for your answer, I will download your last version when it > > will be available. > > > > We found that the difference between Partek GS and xps package is the > > background correction. > > And Partek's support confirmed that Partek GS doesn't use genomic or > > antigenomic background correction but correction like described by > > Professor Bolstad. > > > > But I have an other problem : I would like to perform background > > correction with xps, using the function bgcorrect(), and after to > > export the data.bg.rma for performing the normalization and > > summarization with Partek GS (that take less time than xps) > > I have tried with function export.expr(), export.data and export() > > without success. > > > > How can I do to extract the data.bg.rma into a .txt file, if it was > > possible of course? > > > > Thanks > > Best regards > > > > Arnaud > > > > > > > Date: Mon, 30 Mar 2009 22:33:48 +0200 > > > From: cstrato@aon.at > > > To: arnaudlc@msn.com > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > Partek GS > > > > > > Dear Arnaud, > > > > > > The error you get does only appear on Windows, since MS VC++ requires > > > every C function used to be listed in an Export Definition File > > > "xps.def". Since I have added this function later and do use WinXP only > > > for testing purposes, I have forgotten to add function "MetaProbesets". > > > > > > I have just uploaded a new version xps_1.2.8 to BioC which adds this > > > function to xps.def. > > > You should be able to download the new version in the next one or > > two days. > > > > > > Thank you for reporting this error. I am sorry for the inconvenience. > > > > > > Best regards > > > Christian > > > > > > > > > arnaud Le Cavorzin wrote: > > > > Hi > > > > > > > > I have tried to create a .mps file with xps. But I have got an error, > > > > in french > > > > "Erreur dans .C("MetaProbesets", as.character(schemefile), > > > > as.character(infile), : > > > > le nom C de symbole "MetaProbesets" est introuvable dans la DLL pour > > > > le package "xps" " > > > > That means that there is an error with the name C of symbol > > > > MetaProbesets is missing into the ddl for the package xps. > > > > > > > > I don't understand what it mean, can you help me about this? > > > > > > > > The script used : > > > > > > > > /> xps.rma=validData(data.rma) > > > > > writeLines(rownames(xps.rma),"core.txt") > > > > > metaProbesets(scheme.huex10stv2r2,"core.txt","coreList.mps", > > > > + exonlevel="core")/ > > > > > > > > (I have performed a RMA with > > > > background="antigenomic",option="transcript" and exonlevel="core" > > > > before this, for the comparison with APT and Partek GS) > > > > > > > > Thank you > > > > Best regards > > > > > > > > Arnaud > > > > > > > > > > > > > Date: Sat, 28 Mar 2009 23:03:21 +0100 > > > > > From: cstrato@aon.at > > > > > To: arnaudlc@msn.com > > > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > Partek GS > > > > > > > > > > Dear Arnaud, > > > > > > > > > > Regarding the problem with "bgcorrect.rma()": > > > > > Currently function "bgcorrect.rma()" works only with expression > > arrays, > > > > > I will update it in the next version. > > > > > At the moment you need to use the general function "bgcorrect()" > > with > > > > > the correct settings. > > > > > > > > > > Thus if you want to compute RMA stepwise you need to do: > > > > > ## 1.step: background - rma > > > > > data.bg.rma <- bgcorrect(data.exon, "ExonRMABgrd", filedir=datdir, > > > > > method="rma", select="antigenomic", option="pmonly:epanechnikov", > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > ## 2.step: normalization - quantile > > > > > data.qu.rma <- normalize.quantiles(data.bg.rma, "ExonRMANorm", > > > > > filedir=datdir , exonlevel="core") > > > > > > > > > > ## 3.step: summarization - medpol > > > > > data.mp.rma <- summarize.rma(data.qu.rma, "ExonRMASum", > > filedir=datdir, > > > > > exonlevel="core") > > > > > > > > > > > > > > > This will give the same expression levels as using function "rma()" > > > > > directly: > > > > > ## compute rma: > > > > > data.rma <- rma(data.exon, "ExonRMAcore", filedir=datdir, > > > > > background="antigenomic", > > > > > normalize=T, option="transcript", exonlevel="core") > > > > > > > > > > > > > > > Alternatively you can use function "express()" to compute RMA: > > > > > a, stepwise: > > > > > ## 1.step: background - rma > > > > > expr.bg.rma <- express(data.exon, "ExonExprsBgrd", filedir=datdir, > > > > > tmpdir="", > > > > > bgcorrect.method="rma", bgcorrect.select="antigenomic", > > > > > bgcorrect.option="pmonly:epanechnikov", bgcorrect.params=c(16384), > > > > > exonlevel="core") > > > > > > > > > > ## 2.step: normalization - quantile > > > > > expr.qu.rma <- express(expr.bg.rma, "ExonExprsNorm", filedir=datdir, > > > > > tmpdir="", > > > > > normalize.method="quantile", normalize.select="pmonly", > > > > > normalize.option="transcript:together:none", normalize.logbase="0", > > > > > normalize.params=c(0.0), exonlevel="core") > > > > > > > > > > ## 3.step: summarization - medpol > > > > > expr.mp.rma <- express(expr.qu.rma, "ExonExprsSum", filedir=datdir, > > > > > tmpdir="", > > > > > summarize.method="medianpolish", summarize.select="pmonly", > > > > > summarize.option="transcript", summarize.logbase="log2", > > > > > summarize.params=c(10, 0.01, 1.0), exonlevel="core") > > > > > > > > > > > > > > > b, with a single call to express() > > > > > expr.rma <- express(data.exon, "ExonExprs", filedir=datdir, > > tmpdir="", > > > > > bgcorrect.method="rma", bgcorrect.select="antigenomic", > > > > > bgcorrect.option="pmonly:epanechnikov", bgcorrect.params=c(16384), > > > > > normalize.method="quantile", normalize.select="pmonly", > > > > > normalize.option="transcript:together:none", normalize.logbase="0", > > > > > normalize.params=c(0.0), summarize.method="medianpolish", > > > > > summarize.select="pmonly", summarize.option="transcript", > > > > > summarize.logbase="log2", summarize.params=c(10, 0.01, 1.0), > > > > > exonlevel="core") > > > > > > > > > > > > > > > I hope that these examples help you to use functions bgcorrect(), > > > > > normalize.quantiles(), summarize.rma() and express(). > > > > > > > > > > Best regards > > > > > Christian > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > Hi all. > > > > > > > > > > > > I'm a new user of the xps package and I have some questions about > > > > it and some problems. > > > > > > > > > > > > I use xps package for analysing exon arrays (using Affymetrix > > > > Human Exon 1.0 ST Arrays), and I try to compare the results with the > > > > results obtained with Partek GS. > > > > > > > > > > > > So for that in R I import .CEL files and perform a RMA (using > > > > function rma with xps). I have no problem with this step, it works > > but > > > > I don't obtain the same results with Partek. > > > > > > I have tried different options for rma (xps package) and for > > > > Partek (changing option=transcript or probeset, exonlevel=core or > > > > metacore in xps for example, and do the same thing in Partek) but the > > > > results are always differents. > > > > > > > > > > > > When I import the data from the .CEL files, all is ok, I have the > > > > same results with xps and Partek. But whenever I try a normalization > > > > (RMA) the results are different from the two softwares. > > > > > > I have done for example : > > > > > > > > > > > > > > > > > >> > > > > > > data.probesetnoback.rma=rma(data.huextest,"tmpdt_Huextestprobesetn obackRMA",background="none", > > > > > >> > > > > > > + normalize=TRUE,option="probeset",exonlevel="core",verbose=FALSE) > > > > > > > > > > > >> > > > > > > data.rma=rma(data.huextest,"tmpdt_HuextestRMA",background="antigen omic", > > > > > >> > > > > > > + normalize=TRUE,option="probeset",exonlevel="core",verbose=FALSE) > > > > > > > > > > > >> > > > > > > data.metacore.rma=rma(data.huextest,"tmpdt_HuextestprobesetnobackR MA",background="antigenomic", > > > > > >> > > > > > > + > > normalize=TRUE,option="probeset",exonlevel="metacore",verbose=FALSE) > > > > > > > > > > > > > > > > > > I have also tried with the xps package to perform a background > > > > correction first, after a quantile normalization and finally a > > > > summarization for compare step by step with Partek but it doesn't > > work. > > > > > > > > > > > > I can't perform bgcorrect without error, like for example : > > > > > > > > > > > > > > > > > >> > > > > > > data.qu.rma=bgcorrect.rma(data.huextest,"tmpdt_HuextestbgqumpRMA", filedir=getwd(), > > > > > >> > > > > > > + tmpdir="",exonlevel="core",verbose=FALSE) > > > > > > Erreur dans .local(object, ...) : error in function ‘BgCorrect’ > > > > > > > > > > > >> traceback() > > > > > >> > > > > > > 6: stop(paste("error in function", sQuote("BgCorrect"))) > > > > > > 5: .local(object, ...) > > > > > > 4: xpsBgCorrect(xps.data, filename = filename, filedir = filedir, > > > > > > tmpdir = tmpdir, update = update, select = select, method = > > method, > > > > > > option = option, exonlevel = exonlevel, params = params, > > > > > > verbose = verbose) > > > > > > 3: xpsBgCorrect(xps.data, filename = filename, filedir = filedir, > > > > > > tmpdir = tmpdir, update = update, select = select, method = > > method, > > > > > > option = option, exonlevel = exonlevel, params = params, > > > > > > verbose = verbose) > > > > > > 2: bgcorrect(xps.data, filename = filename, filedir = filedir, > > > > tmpdir = tmpdir, > > > > > > update = update, select = "none", method = "rma", option = > > > > "pmonly:epanechnikov", > > > > > > exonlevel = exonlevel, params = c(16384), verbose = verbose) > > > > > > 1: bgcorrect.rma(data.huextest, "tmpdt_HuextestbgqumpRMA", > > filedir > > > > = getwd(), > > > > > > tmpdir = "", exonlevel = "core", verbose = FALSE) > > > > > > > > > > > >> data.bg.rma=bgcorrect(data.huextest,"tmpdt_HuextestbgqumpRMA", > > > > filedir = getwd(), tmpdir = "", > > > > select="none",method="rma",option="none",exonlevel = "core", > > verbose = > > > > FALSE) > > > > > >> > > > > > > Erreur dans .local(object, ...) : empty parameter list ‘params’ > > > > > > De plus : Warning message: > > > > > > In .local(object, ...) : > > > > > > ‘option’ is different from <pmonly:epanechnikov> for rma > > > > > > > > > > > > > > > > > > If I perform a normalization.quantiles without performing a > > > > bgcorrect it doesn't work, I obtain 0 for all of the values. > > > > > > And summarization give the same kind of error than bgcorrect. > > > > > > > > > > > > Therefore my questions : > > > > > > > > > > > > Why xps pakage and R don't give the same results using the same > > > > setup options? > > > > > > What does exactly xps when performing a RMA? A bgcorrect? A > > > > normalization? > > > > > > > > > > > > Thanks for your answer > > > > > > Best regards > > > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > > > _________________________________________________________________ > > > > > > > > > > > > ? Lancez-vous ! > > > > > > > > > > > > [[alternative HTML version deleted]] > > > > > > > > > > > > > > > > > > > > > > > > ------------------------------------------------------------------ ------ > > > > > > > > > > > > _______________________________________________ > > > > > > Bioconductor mailing list > > > > > > Bioconductor@stat.math.ethz.ch > > > > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > Search the archives: > > > > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > > > > > > > > > > > > ------------------------------------------------------------------ ------ > > > > Tous vos amis discutent sur Messenger, et vous ? Téléchargez > > > > Messenger, c'est gratuit ! <http: get.live.com="" messenger="" overview=""> > > > > > > > ------------------------------------------------------------------ ------ > > Vous voulez savoir ce que vous pouvez faire avec le nouveau Windows > > Live ? Lancez-vous ! > > <http: www.microsoft.com="" windows="" windowslive="" default.aspx=""> > _________________________________________________________________ n ligne... si nouveaux qu'ils ne sont pas encore sortis officiellement sur le marché ! [[alternative HTML version deleted]]
Normalization probe xps Normalization probe xps • 791 views
ADD COMMENT
0
Entering edit mode
cstrato ★ 3.9k
@cstrato-908
Last seen 3.0 years ago
Austria
Dear Arnaud, Yes, xps uses the same background algorithm as APT (and Partek) but uses by default only the probes defined in "exonlevel" for background correction and quantile normalization, i.e. in your case only the "core" probes are used. However, xps offers the possibility to select the probes to be used for background correction, quantile normalization and summarization individually. For RMA normalization you can do: data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, background="antigenomic", normalize=T, option="transcript", exonlevel=c(16316,16316,9216)) This means that probes "core+extended+full+ambiguous+affx" (=16316) are used for background correction and quantile normalization, respectively, and probes "core" (=9216) are used for summarization. As you will see, the results will be more similar to the results obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). However, it is my belief that it is better to use the same probes for all three steps. Maybe this is reflected in your results of finding differentially expressed genes? Best regards Christian arnaud Le Cavorzin wrote: > Hi. > > Thank you for your anwser. > > I have downloaded the last version of xps and performed the > metaProbesets with success. > > > So if I understand, xps use the same background correction algorithm > than Partek or APT. But the difference between these softwares seems > to be caused by the probes used for the background correction. > Moreover it seems to get a difference not only for the background > correction, but for the quantile normalization too, again due to the > different probes. > > In fact I can't get the same results on my samples with xps package > and with Partek. > With Partek we found no expression differences for all of the genes > (after fdr correction), while xps found a little more than 1100 genes > who are differentially expressed (after using prefilter() and > unifilter() function, with the same options than in Partek). > > And I don't know why. > > Thank you for your help and your answer, > Best regards > > Arnaud > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > From: cstrato at aon.at > > To: arnaudlc at msn.com > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu- rennes.fr > > Subject: Re: [BioC] Xps package : errors and RMA difference with > Partek GS > > > > Dear Arnaud, > > > > Let me first answer your question how to export the background data > > (although an example is shown in vignette xps.pdf p.15): > > > > # 1. compute background (or rma) > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", filedir=datdir, > > method="rma", select="antigenomic", option="pmonly:epanechnikov", > > params=c(16384), exonlevel="core") > > > > # 2. find background treenames for data.bg > > getTreeNames(rootFiledata.bg)) > > > > # 3. get background for all trees > > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > > "*", "rbg", "fBg", "BgrdAll.txt") > > # or get background intensity for e.g. tree "BreastA.rbg" > > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > In addition you can also export the background subtracted intensities: > > # 4. get background corrected intensities for all trees > > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > > "*", "int", "fInten", "IntenAll.txt") > > > > However, please note that only the "core" probes are corrected, so I am > > not sure if you can use these data in another program. > > > > > > Some notes on background correction: > > > > Please note that the above background correction does NOT use > > "antigenomic" probes for background correction. The parameter > > select="antigenomic" does only define the kind of MM probes (although > > they are not used in this case). Which probes are used as PM probes is > > defined by exonlevel="core", which means that only "core" probes are > > used for background correction. > > > > As you know, in the RMA background algorithm observed PM probes are > > modeled as the sum of a normal noise component and an exponential > signal > > component. Since in above case only "core" probes are selected as PM > > probes, only these probes are used for background correction. This may > > be the reason why the background data differ from the background data > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > If you want to compute the background using "genomic" or "antigenomic" > > probes and the APT algorithm based on GC content of these probes then > > you need to use: > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", filedir=datdir, > > method="gccontent", select="antigenomic", option="attenuatebg", > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > or the dedicated function: > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", filedir=datdir, > > select="antigenomic", exonlevel="core") > > > > > > Maybe one note on processing time: > > My main goal was to allow processing of exon arrays on computers with > > 1GB RAM only, and to allow access to all interim data such as > background > > intensities and background-corrected probe intensities. Thus, all these > > data are stored as root trees, which means that saving all these > interim > > data on HD is probably the time-consuming step. > > > > I hope that I could answer your questions. > > > > Best regards > > Christian > > > > > > arnaud Le Cavorzin wrote: > > > Hi > > > > > > Thank you for your answer, I will download your last version when it > > > will be available. > > > > > > We found that the difference between Partek GS and xps package is the > > > background correction. > > > And Partek's support confirmed that Partek GS doesn't use genomic or > > > antigenomic background correction but correction like described by > > > Professor Bolstad. > > > > > > But I have an other problem : I would like to perform background > > > correction with xps, using the function bgcorrect(), and after to > > > export the data.bg.rma for performing the normalization and > > > summarization with Partek GS (that take less time than xps) > > > I have tried with function export.expr(), export.data and export() > > > without success. > > > > > > How can I do to extract the data.bg.rma into a .txt file, if it was > > > possible of course? > > > > > > Thanks > > > Best regards > > > > > > Arnaud > > > > > > > > > > Date: Mon, 30 Mar 2009 22:33:48 +0200 > > > > From: cstrato at aon.at > > > > To: arnaudlc at msn.com > > > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu-rennes.fr > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > Partek GS > > > > > > > > Dear Arnaud, > > > > > > > > The error you get does only appear on Windows, since MS VC++ > requires > > > > every C function used to be listed in an Export Definition File > > > > "xps.def". Since I have added this function later and do use > WinXP only > > > > for testing purposes, I have forgotten to add function > "MetaProbesets". > > > > > > > > I have just uploaded a new version xps_1.2.8 to BioC which adds this > > > > function to xps.def. > > > > You should be able to download the new version in the next one or > > > two days. > > > > > > > > Thank you for reporting this error. I am sorry for the > inconvenience. > > > > > > > > Best regards > > > > Christian > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > Hi > > > > > > > > > > I have tried to create a .mps file with xps. But I have got an > error, > > > > > in french > > > > > "Erreur dans .C("MetaProbesets", as.character(schemefile), > > > > > as.character(infile), : > > > > > le nom C de symbole "MetaProbesets" est introuvable dans la > DLL pour > > > > > le package "xps" " > > > > > That means that there is an error with the name C of symbol > > > > > MetaProbesets is missing into the ddl for the package xps. > > > > > > > > > > I don't understand what it mean, can you help me about this? > > > > > > > > > > The script used : > > > > > > > > > > /> xps.rma=validData(data.rma) > > > > > > writeLines(rownames(xps.rma),"core.txt") > > > > > > metaProbesets(scheme.huex10stv2r2,"core.txt","coreList.mps", > > > > > + exonlevel="core")/ > > > > > > > > > > (I have performed a RMA with > > > > > background="antigenomic",option="transcript" and exonlevel="core" > > > > > before this, for the comparison with APT and Partek GS) > > > > > > > > > > Thank you > > > > > Best regards > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > Date: Sat, 28 Mar 2009 23:03:21 +0100 > > > > > > From: cstrato at aon.at > > > > > > To: arnaudlc at msn.com > > > > > > CC: bioconductor at stat.math.ethz.ch; > delphine.rossille at chu-rennes.fr > > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > > Partek GS > > > > > > > > > > > > Dear Arnaud, > > > > > > > > > > > > Regarding the problem with "bgcorrect.rma()": > > > > > > Currently function "bgcorrect.rma()" works only with expression > > > arrays, > > > > > > I will update it in the next version. > > > > > > At the moment you need to use the general function > "bgcorrect()" > > > with > > > > > > the correct settings. > > > > > > > > > > > > Thus if you want to compute RMA stepwise you need to do: > > > > > > ## 1.step: background - rma > > > > > > data.bg.rma <- bgcorrect(data.exon, "ExonRMABgrd", > filedir=datdir, > > > > > > method="rma", select="antigenomic", > option="pmonly:epanechnikov", > > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > > > ## 2.step: normalization - quantile > > > > > > data.qu.rma <- normalize.quantiles(data.bg.rma, "ExonRMANorm", > > > > > > filedir=datdir , exonlevel="core") > > > > > > > > > > > > ## 3.step: summarization - medpol > > > > > > data.mp.rma <- summarize.rma(data.qu.rma, "ExonRMASum", > > > filedir=datdir, > > > > > > exonlevel="core") > > > > > > > > > > > > > > > > > > This will give the same expression levels as using function > "rma()" > > > > > > directly: > > > > > > ## compute rma: > > > > > > data.rma <- rma(data.exon, "ExonRMAcore", filedir=datdir, > > > > > > background="antigenomic", > > > > > > normalize=T, option="transcript", exonlevel="core") > > > > > > > > > > > > > > > > > > Alternatively you can use function "express()" to compute RMA: > > > > > > a, stepwise: > > > > > > ## 1.step: background - rma > > > > > > expr.bg.rma <- express(data.exon, "ExonExprsBgrd", > filedir=datdir, > > > > > > tmpdir="", > > > > > > bgcorrect.method="rma", bgcorrect.select="antigenomic", > > > > > > bgcorrect.option="pmonly:epanechnikov", > bgcorrect.params=c(16384), > > > > > > exonlevel="core") > > > > > > > > > > > > ## 2.step: normalization - quantile > > > > > > expr.qu.rma <- express(expr.bg.rma, "ExonExprsNorm", > filedir=datdir, > > > > > > tmpdir="", > > > > > > normalize.method="quantile", normalize.select="pmonly", > > > > > > normalize.option="transcript:together:none", > normalize.logbase="0", > > > > > > normalize.params=c(0.0), exonlevel="core") > > > > > > > > > > > > ## 3.step: summarization - medpol > > > > > > expr.mp.rma <- express(expr.qu.rma, "ExonExprsSum", > filedir=datdir, > > > > > > tmpdir="", > > > > > > summarize.method="medianpolish", summarize.select="pmonly", > > > > > > summarize.option="transcript", summarize.logbase="log2", > > > > > > summarize.params=c(10, 0.01, 1.0), exonlevel="core") > > > > > > > > > > > > > > > > > > b, with a single call to express() > > > > > > expr.rma <- express(data.exon, "ExonExprs", filedir=datdir, > > > tmpdir="", > > > > > > bgcorrect.method="rma", bgcorrect.select="antigenomic", > > > > > > bgcorrect.option="pmonly:epanechnikov", > bgcorrect.params=c(16384), > > > > > > normalize.method="quantile", normalize.select="pmonly", > > > > > > normalize.option="transcript:together:none", > normalize.logbase="0", > > > > > > normalize.params=c(0.0), summarize.method="medianpolish", > > > > > > summarize.select="pmonly", summarize.option="transcript", > > > > > > summarize.logbase="log2", summarize.params=c(10, 0.01, 1.0), > > > > > > exonlevel="core") > > > > > > > > > > > > > > > > > > I hope that these examples help you to use functions > bgcorrect(), > > > > > > normalize.quantiles(), summarize.rma() and express(). > > > > > > > > > > > > Best regards > > > > > > Christian > > > > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > > Hi all. > > > > > > > > > > > > > > I'm a new user of the xps package and I have some > questions about > > > > > it and some problems. > > > > > > > > > > > > > > I use xps package for analysing exon arrays (using Affymetrix > > > > > Human Exon 1.0 ST Arrays), and I try to compare the results > with the > > > > > results obtained with Partek GS. > > > > > > > > > > > > > > So for that in R I import .CEL files and perform a RMA (using > > > > > function rma with xps). I have no problem with this step, it > works > > > but > > > > > I don't obtain the same results with Partek. > > > > > > > I have tried different options for rma (xps package) and for > > > > > Partek (changing option=transcript or probeset, exonlevel=core or > > > > > metacore in xps for example, and do the same thing in Partek) > but the > > > > > results are always differents. > > > > > > > > > > > > > > When I import the data from the .CEL files, all is ok, I > have the > > > > > same results with xps and Partek. But whenever I try a > normalization > > > > > (RMA) the results are different from the two softwares. > > > > > > > I have done for example : > > > > > > > > > > > > > > > > > > > > >> > > > > > > > > > data.probesetnoback.rma=rma(data.huextest,"tmpdt_Huextestprobesetnob ackRMA",background="none", > > > > > > >> > > > > > > > + > normalize=TRUE,option="probeset",exonlevel="core",verbose=FALSE) > > > > > > > > > > > > > >> > > > > > > > > > data.rma=rma(data.huextest,"tmpdt_HuextestRMA",background="antigenom ic", > > > > > > >> > > > > > > > + > normalize=TRUE,option="probeset",exonlevel="core",verbose=FALSE) > > > > > > > > > > > > > >> > > > > > > > > > data.metacore.rma=rma(data.huextest,"tmpdt_HuextestprobesetnobackRMA ",background="antigenomic", > > > > > > >> > > > > > > > + > > > normalize=TRUE,option="probeset",exonlevel="metacore",verbose=FALSE) > > > > > > > > > > > > > > > > > > > > > I have also tried with the xps package to perform a background > > > > > correction first, after a quantile normalization and finally a > > > > > summarization for compare step by step with Partek but it doesn't > > > work. > > > > > > > > > > > > > > I can't perform bgcorrect without error, like for example : > > > > > > > > > > > > > > > > > > > > >> > > > > > > > > > data.qu.rma=bgcorrect.rma(data.huextest,"tmpdt_HuextestbgqumpRMA",fi ledir=getwd(), > > > > > > >> > > > > > > > + tmpdir="",exonlevel="core",verbose=FALSE) > > > > > > > Erreur dans .local(object, ...) : error in function > ?BgCorrect? > > > > > > > > > > > > > >> traceback() > > > > > > >> > > > > > > > 6: stop(paste("error in function", sQuote("BgCorrect"))) > > > > > > > 5: .local(object, ...) > > > > > > > 4: xpsBgCorrect(xps.data, filename = filename, filedir = > filedir, > > > > > > > tmpdir = tmpdir, update = update, select = select, method = > > > method, > > > > > > > option = option, exonlevel = exonlevel, params = params, > > > > > > > verbose = verbose) > > > > > > > 3: xpsBgCorrect(xps.data, filename = filename, filedir = > filedir, > > > > > > > tmpdir = tmpdir, update = update, select = select, method = > > > method, > > > > > > > option = option, exonlevel = exonlevel, params = params, > > > > > > > verbose = verbose) > > > > > > > 2: bgcorrect(xps.data, filename = filename, filedir = filedir, > > > > > tmpdir = tmpdir, > > > > > > > update = update, select = "none", method = "rma", option = > > > > > "pmonly:epanechnikov", > > > > > > > exonlevel = exonlevel, params = c(16384), verbose = verbose) > > > > > > > 1: bgcorrect.rma(data.huextest, "tmpdt_HuextestbgqumpRMA", > > > filedir > > > > > = getwd(), > > > > > > > tmpdir = "", exonlevel = "core", verbose = FALSE) > > > > > > > > > > > > > >> > data.bg.rma=bgcorrect(data.huextest,"tmpdt_HuextestbgqumpRMA", > > > > > filedir = getwd(), tmpdir = "", > > > > > select="none",method="rma",option="none",exonlevel = "core", > > > verbose = > > > > > FALSE) > > > > > > >> > > > > > > > Erreur dans .local(object, ...) : empty parameter list > ?params? > > > > > > > De plus : Warning message: > > > > > > > In .local(object, ...) : > > > > > > > ?option? is different from <pmonly:epanechnikov> for rma > > > > > > > > > > > > > > > > > > > > > If I perform a normalization.quantiles without performing a > > > > > bgcorrect it doesn't work, I obtain 0 for all of the values. > > > > > > > And summarization give the same kind of error than bgcorrect. > > > > > > > > > > > > > > Therefore my questions : > > > > > > > > > > > > > > Why xps pakage and R don't give the same results using the > same > > > > > setup options? > > > > > > > What does exactly xps when performing a RMA? A bgcorrect? A > > > > > normalization? > > > > > > > > > > > > > > Thanks for your answer > > > > > > > Best regards > > > > > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > > > > > > > _________________________________________________________________ > > > > > > > > > > > > > > ? Lancez-vous ! > > > > > > > > > > > > > > [[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 > > > > > > > > > > > > > > > > > > > > -------------------------------------------------------------------- ---- > > > > > Tous vos amis discutent sur Messenger, et vous ? T?l?chargez > > > > > Messenger, c'est gratuit ! > <http: get.live.com="" messenger="" overview=""> > > > > > > > > > > > -------------------------------------------------------------------- ---- > > > Vous voulez savoir ce que vous pouvez faire avec le nouveau Windows > > > Live ? Lancez-vous ! > > > <http: www.microsoft.com="" windows="" windowslive="" default.aspx=""> > > > > -------------------------------------------------------------------- ---- > T?l?chargez le nouveau Windows Live Messenger ! T?l?chargez Messenger, > c'est gratuit ! <http: get.live.com="" messenger="" overview="">
ADD COMMENT
0
Entering edit mode
Dear Christian, Thank you for your answer. We have tried to perform rma normalization using exonlevel=c(16316,9216,9216) that is to say using all of the probes for the background correction and core probes for the two last steps. And the results seems to be better. We found results closed to those obtained with Partek, and after performing t.test and fdr correction we found the same result with Partek and with xps : no differencially expressed genes even if the p-values and fdr values are a slightly different. In fact there is always 827 genes more with xps than with Partek, so it is maybe the explanation of these differences. So our question : Is it possible to perform rma using only core+extended+full probes for the background correction? If yes what is the "number" needs for this? (core => 9216, metacore => 8192, all => 16316) Thank you very much Best regards Arnaud > Date: Wed, 1 Apr 2009 21:31:54 +0200 > From: cstrato@aon.at > To: arnaudlc@msn.com > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu-rennes.fr > Subject: Re: [BioC] Xps package : errors and RMA difference with Partek GS > > Dear Arnaud, > > Yes, xps uses the same background algorithm as APT (and Partek) but uses > by default only the probes defined in "exonlevel" for background > correction and quantile normalization, i.e. in your case only the "core" > probes are used. > > However, xps offers the possibility to select the probes to be used for > background correction, quantile normalization and summarization > individually. For RMA normalization you can do: > > data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, > background="antigenomic", > normalize=T, option="transcript", > exonlevel=c(16316,16316,9216)) > > This means that probes "core+extended+full+ambiguous+affx" (=16316) are > used for background correction and quantile normalization, respectively, > and probes "core" (=9216) are used for summarization. > > As you will see, the results will be more similar to the results > obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). However, > it is my belief that it is better to use the same probes for all three > steps. Maybe this is reflected in your results of finding differentially > expressed genes? > > Best regards > Christian > > > arnaud Le Cavorzin wrote: > > Hi. > > > > Thank you for your anwser. > > > > I have downloaded the last version of xps and performed the > > metaProbesets with success. > > > > > > So if I understand, xps use the same background correction algorithm > > than Partek or APT. But the difference between these softwares seems > > to be caused by the probes used for the background correction. > > Moreover it seems to get a difference not only for the background > > correction, but for the quantile normalization too, again due to the > > different probes. > > > > In fact I can't get the same results on my samples with xps package > > and with Partek. > > With Partek we found no expression differences for all of the genes > > (after fdr correction), while xps found a little more than 1100 genes > > who are differentially expressed (after using prefilter() and > > unifilter() function, with the same options than in Partek). > > > > And I don't know why. > > > > Thank you for your help and your answer, > > Best regards > > > > Arnaud > > > > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > > From: cstrato@aon.at > > > To: arnaudlc@msn.com > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > Partek GS > > > > > > Dear Arnaud, > > > > > > Let me first answer your question how to export the background data > > > (although an example is shown in vignette xps.pdf p.15): > > > > > > # 1. compute background (or rma) > > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", filedir=datdir, > > > method="rma", select="antigenomic", option="pmonly:epanechnikov", > > > params=c(16384), exonlevel="core") > > > > > > # 2. find background treenames for data.bg > > > getTreeNames(rootFiledata.bg)) > > > > > > # 3. get background for all trees > > > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > > > "*", "rbg", "fBg", "BgrdAll.txt") > > > # or get background intensity for e.g. tree "BreastA.rbg" > > > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > > > In addition you can also export the background subtracted intensities: > > > # 4. get background corrected intensities for all trees > > > export.root(rootFiledata.bg), schemeFiledata.bg), setNamedata.bg), > > > "*", "int", "fInten", "IntenAll.txt") > > > > > > However, please note that only the "core" probes are corrected, so I am > > > not sure if you can use these data in another program. > > > > > > > > > Some notes on background correction: > > > > > > Please note that the above background correction does NOT use > > > "antigenomic" probes for background correction. The parameter > > > select="antigenomic" does only define the kind of MM probes (although > > > they are not used in this case). Which probes are used as PM probes is > > > defined by exonlevel="core", which means that only "core" probes are > > > used for background correction. > > > > > > As you know, in the RMA background algorithm observed PM probes are > > > modeled as the sum of a normal noise component and an exponential > > signal > > > component. Since in above case only "core" probes are selected as PM > > > probes, only these probes are used for background correction. This may > > > be the reason why the background data differ from the background data > > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > > > If you want to compute the background using "genomic" or "antigenomic" > > > probes and the APT algorithm based on GC content of these probes then > > > you need to use: > > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", filedir=datdir, > > > method="gccontent", select="antigenomic", option="attenuatebg", > > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > > or the dedicated function: > > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", filedir=datdir, > > > select="antigenomic", exonlevel="core") > > > > > > > > > Maybe one note on processing time: > > > My main goal was to allow processing of exon arrays on computers with > > > 1GB RAM only, and to allow access to all interim data such as > > background > > > intensities and background-corrected probe intensities. Thus, all these > > > data are stored as root trees, which means that saving all these > > interim > > > data on HD is probably the time-consuming step. > > > > > > I hope that I could answer your questions. > > > > > > Best regards > > > Christian > > > > > > > > > arnaud Le Cavorzin wrote: > > > > Hi > > > > > > > > Thank you for your answer, I will download your last version when it > > > > will be available. > > > > > > > > We found that the difference between Partek GS and xps package is the > > > > background correction. > > > > And Partek's support confirmed that Partek GS doesn't use genomic or > > > > antigenomic background correction but correction like described by > > > > Professor Bolstad. > > > > > > > > But I have an other problem : I would like to perform background > > > > correction with xps, using the function bgcorrect(), and after to > > > > export the data.bg.rma for performing the normalization and > > > > summarization with Partek GS (that take less time than xps) > > > > I have tried with function export.expr(), export.data and export() > > > > without success. > > > > > > > > How can I do to extract the data.bg.rma into a .txt file, if it was > > > > possible of course? > > > > > > > > Thanks > > > > Best regards > > > > > > > > Arnaud > > > > > > > > > > > > > Date: Mon, 30 Mar 2009 22:33:48 +0200 > > > > > From: cstrato@aon.at > > > > > To: arnaudlc@msn.com > > > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > Partek GS > > > > > > > > > > Dear Arnaud, > > > > > > > > > > The error you get does only appear on Windows, since MS VC++ > > requires > > > > > every C function used to be listed in an Export Definition File > > > > > "xps.def". Since I have added this function later and do use > > WinXP only > > > > > for testing purposes, I have forgotten to add function > > "MetaProbesets". > > > > > > > > > > I have just uploaded a new version xps_1.2.8 to BioC which adds this > > > > > function to xps.def. > > > > > You should be able to download the new version in the next one or > > > > two days. > > > > > > > > > > Thank you for reporting this error. I am sorry for the > > inconvenience. > > > > > > > > > > Best regards > > > > > Christian > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > Hi > > > > > > > > > > > > I have tried to create a .mps file with xps. But I have got an > > error, > > > > > > in french > > > > > > "Erreur dans .C("MetaProbesets", as.character(schemefile), > > > > > > as.character(infile), : > > > > > > le nom C de symbole "MetaProbesets" est introuvable dans la > > DLL pour > > > > > > le package "xps" " > > > > > > That means that there is an error with the name C of symbol > > > > > > MetaProbesets is missing into the ddl for the package xps. > > > > > > > > > > > > I don't understand what it mean, can you help me about this? > > > > > > > > > > > > The script used : > > > > > > > > > > > > /> xps.rma=validData(data.rma) > > > > > > > writeLines(rownames(xps.rma),"core.txt") > > > > > > > metaProbesets(scheme.huex10stv2r2,"core.txt","coreList.mps", > > > > > > + exonlevel="core")/ > > > > > > > > > > > > (I have performed a RMA with > > > > > > background="antigenomic",option="transcript" and exonlevel="core" > > > > > > before this, for the comparison with APT and Partek GS) > > > > > > > > > > > > Thank you > > > > > > Best regards > > > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > > > > Date: Sat, 28 Mar 2009 23:03:21 +0100 > > > > > > > From: cstrato@aon.at > > > > > > > To: arnaudlc@msn.com > > > > > > > CC: bioconductor@stat.math.ethz.ch; > > delphine.rossille@chu-rennes.fr > > > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > > > Partek GS > > > > > > > > > > > > > > Dear Arnaud, > > > > > > > > > > > > > > Regarding the problem with "bgcorrect.rma()": > > > > > > > Currently function "bgcorrect.rma()" works only with expression > > > > arrays, > > > > > > > I will update it in the next version. > > > > > > > At the moment you need to use the general function > > "bgcorrect()" > > > > with > > > > > > > the correct settings. > > > > > > > > > > > > > > Thus if you want to compute RMA stepwise you need to do: > > > > > > > ## 1.step: background - rma > > > > > > > data.bg.rma <- bgcorrect(data.exon, "ExonRMABgrd", > > filedir=datdir, > > > > > > > method="rma", select="antigenomic", > > option="pmonly:epanechnikov", > > > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > > > > > ## 2.step: normalization - quantile > > > > > > > data.qu.rma <- normalize.quantiles(data.bg.rma, "ExonRMANorm", > > > > > > > filedir=datdir , exonlevel="core") > > > > > > > > > > > > > > ## 3.step: summarization - medpol > > > > > > > data.mp.rma <- summarize.rma(data.qu.rma, "ExonRMASum", > > > > filedir=datdir, > > > > > > > exonlevel="core") > > > > > > > > > > > > > > > > > > > > > This will give the same expression levels as using function > > "rma()" > > > > > > > directly: > > > > > > > ## compute rma: > > > > > > > data.rma <- rma(data.exon, "ExonRMAcore", filedir=datdir, > > > > > > > background="antigenomic", > > > > > > > normalize=T, option="transcript", exonlevel="core") > > > > > > > > > > > > > > > > > > > > > Alternatively you can use function "express()" to compute RMA: > > > > > > > a, stepwise: > > > > > > > ## 1.step: background - rma > > > > > > > expr.bg.rma <- express(data.exon, "ExonExprsBgrd", > > filedir=datdir, > > > > > > > tmpdir="", > > > > > > > bgcorrect.method="rma", bgcorrect.select="antigenomic", > > > > > > > bgcorrect.option="pmonly:epanechnikov", > > bgcorrect.params=c(16384), > > > > > > > exonlevel="core") > > > > > > > > > > > > > > ## 2.step: normalization - quantile > > > > > > > expr.qu.rma <- express(expr.bg.rma, "ExonExprsNorm", > > filedir=datdir, > > > > > > > tmpdir="", > > > > > > > normalize.method="quantile", normalize.select="pmonly", > > > > > > > normalize.option="transcript:together:none", > > normalize.logbase="0", > > > > > > > normalize.params=c(0.0), exonlevel="core") > > > > > > > > > > > > > > ## 3.step: summarization - medpol > > > > > > > expr.mp.rma <- express(expr.qu.rma, "ExonExprsSum", > > filedir=datdir, > > > > > > > tmpdir="", > > > > > > > summarize.method="medianpolish", summarize.select="pmonly", > > > > > > > summarize.option="transcript", summarize.logbase="log2", > > > > > > > summarize.params=c(10, 0.01, 1.0), exonlevel="core") > > > > > > > > > > > > > > > > > > > > > b, with a single call to express() > > > > > > > expr.rma <- express(data.exon, "ExonExprs", filedir=datdir, > > > > tmpdir="", > > > > > > > bgcorrect.method="rma", bgcorrect.select="antigenomic", > > > > > > > bgcorrect.option="pmonly:epanechnikov", > > bgcorrect.params=c(16384), > > > > > > > normalize.method="quantile", normalize.select="pmonly", > > > > > > > normalize.option="transcript:together:none", > > normalize.logbase="0", > > > > > > > normalize.params=c(0.0), summarize.method="medianpolish", > > > > > > > summarize.select="pmonly", summarize.option="transcript", > > > > > > > summarize.logbase="log2", summarize.params=c(10, 0.01, 1.0), > > > > > > > exonlevel="core") > > > > > > > > > > > > > > > > > > > > > I hope that these examples help you to use functions > > bgcorrect(), > > > > > > > normalize.quantiles(), summarize.rma() and express(). > > > > > > > > > > > > > > Best regards > > > > > > > Christian > > > > > > > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > > > Hi all. > > > > > > > > > > > > > > > > I'm a new user of the xps package and I have some > > questions about > > > > > > it and some problems. > > > > > > > > > > > > > > > > I use xps package for analysing exon arrays (using Affymetrix > > > > > > Human Exon 1.0 ST Arrays), and I try to compare the results > > with the > > > > > > results obtained with Partek GS. > > > > > > > > > > > > > > > > So for that in R I import .CEL files and perform a RMA (using > > > > > > function rma with xps). I have no problem with this step, it > > works > > > > but > > > > > > I don't obtain the same results with Partek. > > > > > > > > I have tried different options for rma (xps package) and for > > > > > > Partek (changing option=transcript or probeset, exonlevel=core or > > > > > > metacore in xps for example, and do the same thing in Partek) > > but the > > > > > > results are always differents. > > > > > > > > > > > > > > > > When I import the data from the .CEL files, all is ok, I > > have the > > > > > > same results with xps and Partek. But whenever I try a > > normalization > > > > > > (RMA) the results are different from the two softwares. > > > > > > > > I have done for example : > > > > > > > > > > > > > > > > > > > > > > > >> > > > > > > > > > > > > data.probesetnoback.rma=rma(data.huextest,"tmpdt_Huextestprobesetn obackRMA",background="none", > > > > > > > >> > > > > > > > > + > > normalize=TRUE,option="probeset",exonlevel="core",verbose=FALSE) > > > > > > > > > > > > > > > >> > > > > > > > > > > > > data.rma=rma(data.huextest,"tmpdt_HuextestRMA",background="antigen omic", > > > > > > > >> > > > > > > > > + > > normalize=TRUE,option="probeset",exonlevel="core",verbose=FALSE) > > > > > > > > > > > > > > > >> > > > > > > > > > > > > data.metacore.rma=rma(data.huextest,"tmpdt_HuextestprobesetnobackR MA",background="antigenomic", > > > > > > > >> > > > > > > > > + > > > > normalize=TRUE,option="probeset",exonlevel="metacore",verbose=FALSE) > > > > > > > > > > > > > > > > > > > > > > > > I have also tried with the xps package to perform a background > > > > > > correction first, after a quantile normalization and finally a > > > > > > summarization for compare step by step with Partek but it doesn't > > > > work. > > > > > > > > > > > > > > > > I can't perform bgcorrect without error, like for example : > > > > > > > > > > > > > > > > > > > > > > > >> > > > > > > > > > > > > data.qu.rma=bgcorrect.rma(data.huextest,"tmpdt_HuextestbgqumpRMA", filedir=getwd(), > > > > > > > >> > > > > > > > > + tmpdir="",exonlevel="core",verbose=FALSE) > > > > > > > > Erreur dans .local(object, ...) : error in function > > ‘BgCorrect’ > > > > > > > > > > > > > > > >> traceback() > > > > > > > >> > > > > > > > > 6: stop(paste("error in function", sQuote("BgCorrect"))) > > > > > > > > 5: .local(object, ...) > > > > > > > > 4: xpsBgCorrect(xps.data, filename = filename, filedir = > > filedir, > > > > > > > > tmpdir = tmpdir, update = update, select = select, method = > > > > method, > > > > > > > > option = option, exonlevel = exonlevel, params = params, > > > > > > > > verbose = verbose) > > > > > > > > 3: xpsBgCorrect(xps.data, filename = filename, filedir = > > filedir, > > > > > > > > tmpdir = tmpdir, update = update, select = select, method = > > > > method, > > > > > > > > option = option, exonlevel = exonlevel, params = params, > > > > > > > > verbose = verbose) > > > > > > > > 2: bgcorrect(xps.data, filename = filename, filedir = filedir, > > > > > > tmpdir = tmpdir, > > > > > > > > update = update, select = "none", method = "rma", option = > > > > > > "pmonly:epanechnikov", > > > > > > > > exonlevel = exonlevel, params = c(16384), verbose = verbose) > > > > > > > > 1: bgcorrect.rma(data.huextest, "tmpdt_HuextestbgqumpRMA", > > > > filedir > > > > > > = getwd(), > > > > > > > > tmpdir = "", exonlevel = "core", verbose = FALSE) > > > > > > > > > > > > > > > >> > > data.bg.rma=bgcorrect(data.huextest,"tmpdt_HuextestbgqumpRMA", > > > > > > filedir = getwd(), tmpdir = "", > > > > > > select="none",method="rma",option="none",exonlevel = "core", > > > > verbose = > > > > > > FALSE) > > > > > > > >> > > > > > > > > Erreur dans .local(object, ...) : empty parameter list > > ‘params’ > > > > > > > > De plus : Warning message: > > > > > > > > In .local(object, ...) : > > > > > > > > ‘option’ is different from <pmonly:epanechnikov> for rma > > > > > > > > > > > > > > > > > > > > > > > > If I perform a normalization.quantiles without performing a > > > > > > bgcorrect it doesn't work, I obtain 0 for all of the values. > > > > > > > > And summarization give the same kind of error than bgcorrect. > > > > > > > > > > > > > > > > Therefore my questions : > > > > > > > > > > > > > > > > Why xps pakage and R don't give the same results using the > > same > > > > > > setup options? > > > > > > > > What does exactly xps when performing a RMA? A bgcorrect? A > > > > > > normalization? > > > > > > > > > > > > > > > > Thanks for your answer > > > > > > > > Best regards > > > > > > > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > > > > > > > > > > > _________________________________________________________________ > > > > > > > > > > > > > > > > ? Lancez-vous ! > > > > > > > > > > > > > > > > [[alternative HTML version deleted]] > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > ------------------------------------------------------------------ ------ > > > > > > > > > > > > > > > > _______________________________________________ > > > > > > > > Bioconductor mailing list > > > > > > > > Bioconductor@stat.math.ethz.ch > > > > > > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > > > > Search the archives: > > > > > > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > > > > > > > > > > > > > > > > > > > > > > ------------------------------------------------------------------ ------ > > > > > > Tous vos amis discutent sur Messenger, et vous ? Téléchargez > > > > > > Messenger, c'est gratuit ! > > <http: get.live.com="" messenger="" overview=""> > > > > > > > > > > > > > > > ------------------------------------------------------------------ ------ > > > > Vous voulez savoir ce que vous pouvez faire avec le nouveau Windows > > > > Live ? Lancez-vous ! > > > > <http: www.microsoft.com="" windows="" windowslive="" default.aspx=""> > > > > > > > ------------------------------------------------------------------ ------ > > Téléchargez le nouveau Windows Live Messenger ! Téléchargez Messenger, > > c'est gratuit ! <http: get.live.com="" messenger="" overview=""> > _________________________________________________________________ Découvrez toutes les possibilités de communication avec vos proches [[alternative HTML version deleted]]
ADD REPLY
0
Entering edit mode
Dear Arnaud, Let me answer your questions individually: 1. Differential expression: As you see, package xps is quite flexible and allows to use different probes for background correction, quantile normalization and summarization. This may have effects on the results you get with the different settings. Thus, the most important question to determine which setting to choose, is: Do you expect to find differentially expressed genes in your experiment or not? Since the different settings give different results the best way would be to confirm one result or the other experimentally, e.g. by doing PCR with some of the genes in question. 2. Number of "core" genes: Please note that the number of "core" transcripts is defined in the Affymetrix probeset annotation file. Only probesets with "level=Core" are combined as "core" transcripts, and only the subset with "crosshyb_type=unique" is used as "metacore" transcripts. Thus the number of "core" transcripts depends on the annotation used. For the current Affymetrix probeset annotation file version "HuEx-1_0-st-v2.na28.hg18.probeset.csv" you get 18708 "core" transcripts and 17880 "metacore" transcripts. Since the difference is 828 transcripts, I assume that you are only using the "metacore" transcripts with Partek. Thus, in xps you need to use exonlevel="metacore". 3. Probes used for background correction: Package xps contains an internal function exonLevel() which is used to convert parameter "exonlevel" to an integer. Because of your question I have decided to make this function public in version xps_1.2.9, which you should be able to download on Monday. The corresponding helpfile "?exonLevel" will explain the different integers to be used. For your convenience the integers are: core (=8192+1024), extended (=4096+512), full (=2048+256), ambiguous (=128), affx(=60). Thus "core+extended+full" is 16128. Coincidently, the question which probes to be used for background correction was recently asked also at: http://groups.google.com/group/aroma- affymetrix/browse_thread/thread/69fdc2757894f290# Best regards Christian arnaud Le Cavorzin wrote: > Dear Christian, > > Thank you for your answer. > > We have tried to perform rma normalization using > exonlevel=c(16316,9216,9216) that is to say using all of the probes > for the background correction and core probes for the two last steps. > And the results seems to be better. We found results closed to those > obtained with Partek, and after performing t.test and fdr correction > we found the same result with Partek and with xps : no differencially > expressed genes even if the p-values and fdr values are a slightly > different. > > In fact there is always 827 genes more with xps than with Partek, so > it is maybe the explanation of these differences. > So our question : Is it possible to perform rma using only > core+extended+full probes for the background correction? If yes what > is the "number" needs for this? (core => 9216, metacore => 8192, all > => 16316) > > Thank you very much > Best regards > > Arnaud > > > > Date: Wed, 1 Apr 2009 21:31:54 +0200 > > From: cstrato at aon.at > > To: arnaudlc at msn.com > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu- rennes.fr > > Subject: Re: [BioC] Xps package : errors and RMA difference with > Partek GS > > > > Dear Arnaud, > > > > Yes, xps uses the same background algorithm as APT (and Partek) but > uses > > by default only the probes defined in "exonlevel" for background > > correction and quantile normalization, i.e. in your case only the > "core" > > probes are used. > > > > However, xps offers the possibility to select the probes to be used for > > background correction, quantile normalization and summarization > > individually. For RMA normalization you can do: > > > > data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, > > background="antigenomic", > > normalize=T, option="transcript", > > exonlevel=c(16316,16316,9216)) > > > > This means that probes "core+extended+full+ambiguous+affx" (=16316) are > > used for background correction and quantile normalization, > respectively, > > and probes "core" (=9216) are used for summarization. > > > > As you will see, the results will be more similar to the results > > obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). However, > > it is my belief that it is better to use the same probes for all three > > steps. Maybe this is reflected in your results of finding > differentially > > expressed genes? > > > > Best regards > > Christian > > > > > > arnaud Le Cavorzin wrote: > > > Hi. > > > > > > Thank you for your anwser. > > > > > > I have downloaded the last version of xps and performed the > > > metaProbesets with success. > > > > > > > > > So if I understand, xps use the same background correction algorithm > > > than Partek or APT. But the difference between these softwares seems > > > to be caused by the probes used for the background correction. > > > Moreover it seems to get a difference not only for the background > > > correction, but for the quantile normalization too, again due to the > > > different probes. > > > > > > In fact I can't get the same results on my samples with xps package > > > and with Partek. > > > With Partek we found no expression differences for all of the genes > > > (after fdr correction), while xps found a little more than 1100 genes > > > who are differentially expressed (after using prefilter() and > > > unifilter() function, with the same options than in Partek). > > > > > > And I don't know why. > > > > > > Thank you for your help and your answer, > > > Best regards > > > > > > Arnaud > > > > > > > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > > > From: cstrato at aon.at > > > > To: arnaudlc at msn.com > > > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu-rennes.fr > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > Partek GS > > > > > > > > Dear Arnaud, > > > > > > > > Let me first answer your question how to export the background data > > > > (although an example is shown in vignette xps.pdf p.15): > > > > > > > > # 1. compute background (or rma) > > > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", filedir=datdir, > > > > method="rma", select="antigenomic", option="pmonly:epanechnikov", > > > > params=c(16384), exonlevel="core") > > > > > > > > # 2. find background treenames for data.bg > > > > getTreeNamesrootFiledata.bg)) > > > > > > > > # 3. get background for all trees > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > setNamedata.bg), > > > > "*", "rbg", "fBg", "BgrdAll.txt") > > > > # or get background intensity for e.g. tree "BreastA.rbg" > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > setNamedata.bg), > > > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > > > > > In addition you can also export the background subtracted > intensities: > > > > # 4. get background corrected intensities for all trees > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > setNamedata.bg), > > > > "*", "int", "fInten", "IntenAll.txt") > > > > > > > > However, please note that only the "core" probes are corrected, > so I am > > > > not sure if you can use these data in another program. > > > > > > > > > > > > Some notes on background correction: > > > > > > > > Please note that the above background correction does NOT use > > > > "antigenomic" probes for background correction. The parameter > > > > select="antigenomic" does only define the kind of MM probes > (although > > > > they are not used in this case). Which probes are used as PM > probes is > > > > defined by exonlevel="core", which means that only "core" probes are > > > > used for background correction. > > > > > > > > As you know, in the RMA background algorithm observed PM probes are > > > > modeled as the sum of a normal noise component and an exponential > > > signal > > > > component. Since in above case only "core" probes are selected as PM > > > > probes, only these probes are used for background correction. > This may > > > > be the reason why the background data differ from the background > data > > > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > > > > > If you want to compute the background using "genomic" or > "antigenomic" > > > > probes and the APT algorithm based on GC content of these probes > then > > > > you need to use: > > > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", filedir=datdir, > > > > method="gccontent", select="antigenomic", option="attenuatebg", > > > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > > > or the dedicated function: > > > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", filedir=datdir, > > > > select="antigenomic", exonlevel="core") > > > > > > > > > > > > Maybe one note on processing time: > > > > My main goal was to allow processing of exon arrays on computers > with > > > > 1GB RAM only, and to allow access to all interim data such as > > > background > > > > intensities and background-corrected probe intensities. Thus, > all these > > > > data are stored as root trees, which means that saving all these > > > interim > > > > data on HD is probably the time-consuming step. > > > > > > > > I hope that I could answer your questions. > > > > > > > > Best regards > > > > Christian > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > Hi > > > > > > > > > > Thank you for your answer, I will download your last version > when it > > > > > will be available. > > > > > > > > > > We found that the difference between Partek GS and xps package > is the > > > > > background correction. > > > > > And Partek's support confirmed that Partek GS doesn't use > genomic or > > > > > antigenomic background correction but correction like described by > > > > > Professor Bolstad. > > > > > > > > > > But I have an other problem : I would like to perform background > > > > > correction with xps, using the function bgcorrect(), and after to > > > > > export the data.bg.rma for performing the normalization and > > > > > summarization with Partek GS (that take less time than xps) > > > > > I have tried with function export.expr(), export.data and export() > > > > > without success. > > > > > > > > > > How can I do to extract the data.bg.rma into a .txt file, if > it was > > > > > possible of course? > > > > > > > > > > Thanks > > > > > Best regards > > > > > > > > > > Arnaud
ADD REPLY
0
Entering edit mode
Dear Christian, Thank you very much for your answer and for your reactivity. We have performed RMA normalization using exonlevel=c(16316,8192,8192) that is to say using all probes for the background correction and only metacore probes for the quantile normalization and summarization, so the same options using by Partek ("core" in Partek corresponding to "metacore" in xps, like you have suggested it). We get the same number of probeset with Partek and xps package, but the results are differents for the two softwares. Even if it was better, we found 3539 genes with a p-value<0.05 with xps, and 3337 genes with p-value<0.05 with Partek, and the results remain different for the two softwares. We don't obtain the same p-value, in particular because we don't obtain the same means. I have also imported the data.rma from xps to Partek, and performed the t test with Partek : the results are the same than performing unifilter with xps, we obtain the same p value than with xps and the same means. So they are still different than the results using Partek only. (Confirm that there is a difference with the probes used in Partek for the RMA normalization) Another question : when I use fdr correction or no correction with xps, the results are still the same. Only when I use bonferroni correction the p-adjusted change. I don't understand why FDR correction have no effect. > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALSE, 0.95,TRUE), + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE,0 .95,TRUE), + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) Thanks Best regards, Arnaud > Date: Fri, 3 Apr 2009 20:25:15 +0200 > From: cstrato@aon.at > To: arnaudlc@msn.com > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu-rennes.fr > Subject: Re: [BioC] Xps package : errors and RMA difference with Partek GS > > Dear Arnaud, > > Let me answer your questions individually: > > 1. Differential expression: > As you see, package xps is quite flexible and allows to use different > probes for background correction, quantile normalization and > summarization. This may have effects on the results you get with the > different settings. Thus, the most important question to determine which > setting to choose, is: > Do you expect to find differentially expressed genes in your experiment > or not? > Since the different settings give different results the best way would > be to confirm one result or the other experimentally, e.g. by doing PCR > with some of the genes in question. > > 2. Number of "core" genes: > Please note that the number of "core" transcripts is defined in the > Affymetrix probeset annotation file. Only probesets with "level=Core" > are combined as "core" transcripts, and only the subset with > "crosshyb_type=unique" is used as "metacore" transcripts. Thus the > number of "core" transcripts depends on the annotation used. For the > current Affymetrix probeset annotation file version > "HuEx-1_0-st-v2.na28.hg18.probeset.csv" you get 18708 "core" transcripts > and 17880 "metacore" transcripts. Since the difference is 828 > transcripts, I assume that you are only using the "metacore" transcripts > with Partek. Thus, in xps you need to use exonlevel="metacore". > > 3. Probes used for background correction: > Package xps contains an internal function exonLevel() which is used to > convert parameter "exonlevel" to an integer. Because of your question I > have decided to make this function public in version xps_1.2.9, which > you should be able to download on Monday. The corresponding helpfile > "?exonLevel" will explain the different integers to be used. > For your convenience the integers are: core (=8192+1024), extended > (=4096+512), full (=2048+256), ambiguous (=128), affx(=60). Thus > "core+extended+full" is 16128. > > Coincidently, the question which probes to be used for background > correction was recently asked also at: > http://groups.google.com/group/aroma- affymetrix/browse_thread/thread/69fdc2757894f290# > > Best regards > Christian > > > arnaud Le Cavorzin wrote: > > Dear Christian, > > > > Thank you for your answer. > > > > We have tried to perform rma normalization using > > exonlevel=c(16316,9216,9216) that is to say using all of the probes > > for the background correction and core probes for the two last steps. > > And the results seems to be better. We found results closed to those > > obtained with Partek, and after performing t.test and fdr correction > > we found the same result with Partek and with xps : no differencially > > expressed genes even if the p-values and fdr values are a slightly > > different. > > > > In fact there is always 827 genes more with xps than with Partek, so > > it is maybe the explanation of these differences. > > So our question : Is it possible to perform rma using only > > core+extended+full probes for the background correction? If yes what > > is the "number" needs for this? (core => 9216, metacore => 8192, all > > => 16316) > > > > Thank you very much > > Best regards > > > > Arnaud > > > > > > > Date: Wed, 1 Apr 2009 21:31:54 +0200 > > > From: cstrato@aon.at > > > To: arnaudlc@msn.com > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > Partek GS > > > > > > Dear Arnaud, > > > > > > Yes, xps uses the same background algorithm as APT (and Partek) but > > uses > > > by default only the probes defined in "exonlevel" for background > > > correction and quantile normalization, i.e. in your case only the > > "core" > > > probes are used. > > > > > > However, xps offers the possibility to select the probes to be used for > > > background correction, quantile normalization and summarization > > > individually. For RMA normalization you can do: > > > > > > data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, > > > background="antigenomic", > > > normalize=T, option="transcript", > > > exonlevel=c(16316,16316,9216)) > > > > > > This means that probes "core+extended+full+ambiguous+affx" (=16316) are > > > used for background correction and quantile normalization, > > respectively, > > > and probes "core" (=9216) are used for summarization. > > > > > > As you will see, the results will be more similar to the results > > > obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). However, > > > it is my belief that it is better to use the same probes for all three > > > steps. Maybe this is reflected in your results of finding > > differentially > > > expressed genes? > > > > > > Best regards > > > Christian > > > > > > > > > arnaud Le Cavorzin wrote: > > > > Hi. > > > > > > > > Thank you for your anwser. > > > > > > > > I have downloaded the last version of xps and performed the > > > > metaProbesets with success. > > > > > > > > > > > > So if I understand, xps use the same background correction algorithm > > > > than Partek or APT. But the difference between these softwares seems > > > > to be caused by the probes used for the background correction. > > > > Moreover it seems to get a difference not only for the background > > > > correction, but for the quantile normalization too, again due to the > > > > different probes. > > > > > > > > In fact I can't get the same results on my samples with xps package > > > > and with Partek. > > > > With Partek we found no expression differences for all of the genes > > > > (after fdr correction), while xps found a little more than 1100 genes > > > > who are differentially expressed (after using prefilter() and > > > > unifilter() function, with the same options than in Partek). > > > > > > > > And I don't know why. > > > > > > > > Thank you for your help and your answer, > > > > Best regards > > > > > > > > Arnaud > > > > > > > > > > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > > > > From: cstrato@aon.at > > > > > To: arnaudlc@msn.com > > > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > Partek GS > > > > > > > > > > Dear Arnaud, > > > > > > > > > > Let me first answer your question how to export the background data > > > > > (although an example is shown in vignette xps.pdf p.15): > > > > > > > > > > # 1. compute background (or rma) > > > > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", filedir=datdir, > > > > > method="rma", select="antigenomic", option="pmonly:epanechnikov", > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > # 2. find background treenames for data.bg > > > > > getTreeNamesrootFiledata.bg)) > > > > > > > > > > # 3. get background for all trees > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > setNamedata.bg), > > > > > "*", "rbg", "fBg", "BgrdAll.txt") > > > > > # or get background intensity for e.g. tree "BreastA.rbg" > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > setNamedata.bg), > > > > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > > > > > > > In addition you can also export the background subtracted > > intensities: > > > > > # 4. get background corrected intensities for all trees > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > setNamedata.bg), > > > > > "*", "int", "fInten", "IntenAll.txt") > > > > > > > > > > However, please note that only the "core" probes are corrected, > > so I am > > > > > not sure if you can use these data in another program. > > > > > > > > > > > > > > > Some notes on background correction: > > > > > > > > > > Please note that the above background correction does NOT use > > > > > "antigenomic" probes for background correction. The parameter > > > > > select="antigenomic" does only define the kind of MM probes > > (although > > > > > they are not used in this case). Which probes are used as PM > > probes is > > > > > defined by exonlevel="core", which means that only "core" probes are > > > > > used for background correction. > > > > > > > > > > As you know, in the RMA background algorithm observed PM probes are > > > > > modeled as the sum of a normal noise component and an exponential > > > > signal > > > > > component. Since in above case only "core" probes are selected as PM > > > > > probes, only these probes are used for background correction. > > This may > > > > > be the reason why the background data differ from the background > > data > > > > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > > > > > > > If you want to compute the background using "genomic" or > > "antigenomic" > > > > > probes and the APT algorithm based on GC content of these probes > > then > > > > > you need to use: > > > > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", filedir=datdir, > > > > > method="gccontent", select="antigenomic", option="attenuatebg", > > > > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > > > > or the dedicated function: > > > > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", filedir=datdir, > > > > > select="antigenomic", exonlevel="core") > > > > > > > > > > > > > > > Maybe one note on processing time: > > > > > My main goal was to allow processing of exon arrays on computers > > with > > > > > 1GB RAM only, and to allow access to all interim data such as > > > > background > > > > > intensities and background-corrected probe intensities. Thus, > > all these > > > > > data are stored as root trees, which means that saving all these > > > > interim > > > > > data on HD is probably the time-consuming step. > > > > > > > > > > I hope that I could answer your questions. > > > > > > > > > > Best regards > > > > > Christian > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > Hi > > > > > > > > > > > > Thank you for your answer, I will download your last version > > when it > > > > > > will be available. > > > > > > > > > > > > We found that the difference between Partek GS and xps package > > is the > > > > > > background correction. > > > > > > And Partek's support confirmed that Partek GS doesn't use > > genomic or > > > > > > antigenomic background correction but correction like described by > > > > > > Professor Bolstad. > > > > > > > > > > > > But I have an other problem : I would like to perform background > > > > > > correction with xps, using the function bgcorrect(), and after to > > > > > > export the data.bg.rma for performing the normalization and > > > > > > summarization with Partek GS (that take less time than xps) > > > > > > I have tried with function export.expr(), export.data and export() > > > > > > without success. > > > > > > > > > > > > How can I do to extract the data.bg.rma into a .txt file, if > > it was > > > > > > possible of course? > > > > > > > > > > > > Thanks > > > > > > Best regards > > > > > > > > > > > > Arnaud > _________________________________________________________________ ? Lancez-vous ! [[alternative HTML version deleted]]
ADD REPLY
0
Entering edit mode
Dear Arnaud, As I said already in an earlier mail, when comparing xps and APT I get slightly different expression levels, which may be due to different probes used for background correction and quantile normalization since the results obtained for median-polish only are identical (see vignette APTvsXPS.pdf). For this reason the means are also slightly different, and thus also the p-values. Furthermore, in order to compare xps and APT I needed to create a metaprobeset file using function "metaProbesets()" which I used with APT. This file assures that the same metacore probesets are used by both programs. Please note that cannot comment on differences between xps and any commercial software, I can only compare xps to other open-source programs such as affy and APT. However, if you compare your software to APT and the results are identical, then this would answer your question. Regarding fdr adjustment you will see that "validData(rma.ufr)" lists only the probesets which satisfy the condition pval<0.05. In order to see all probesets you need to do "validData(rma.ufr,"UnitName")" or simply "rma.ufr at data". (I must admit that I need to document this feature in the help file.) Then you should see also differences between p-value and p-adjusted. But I need to investigate further. Best regards Christian arnaud Le Cavorzin wrote: > Dear Christian, > > Thank you very much for your answer and for your reactivity. > > We have performed RMA normalization using exonlevel=c(16316,8192,8192) > that is to say using all probes for the background correction and only > metacore probes for the quantile normalization and summarization, so > the same options using by Partek ("core" in Partek corresponding to > "metacore" in xps, like you have suggested it). > > We get the same number of probeset with Partek and xps package, but > the results are differents for the two softwares. > Even if it was better, we found 3539 genes with a p-value<0.05 with > xps, and 3337 genes with p-value<0.05 with Partek, and the results > remain different for the two softwares. We don't obtain the same > p-value, in particular because we don't obtain the same means. > > I have also imported the data.rma from xps to Partek, and performed > the t test with Partek : the results are the same than performing > unifilter with xps, we obtain the same p value than with xps and the > same means. So they are still different than the results using Partek > only. > (Confirm that there is a difference with the probes used in Partek for > the RMA normalization) > > Another question : when I use fdr correction or no correction with > xps, the results are still the same. Only when I use bonferroni > correction the p-adjusted change. I don't understand why FDR > correction have no effect. > > /> > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALSE, 0.95,TRUE), > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > / > /> > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE,0 .95,TRUE), > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > Thanks > Best regards, > > Arnaud > > > > Date: Fri, 3 Apr 2009 20:25:15 +0200 > > From: cstrato at aon.at > > To: arnaudlc at msn.com > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu- rennes.fr > > Subject: Re: [BioC] Xps package : errors and RMA difference with > Partek GS > > > > Dear Arnaud, > > > > Let me answer your questions individually: > > > > 1. Differential expression: > > As you see, package xps is quite flexible and allows to use different > > probes for background correction, quantile normalization and > > summarization. This may have effects on the results you get with the > > different settings. Thus, the most important question to determine > which > > setting to choose, is: > > Do you expect to find differentially expressed genes in your experiment > > or not? > > Since the different settings give different results the best way would > > be to confirm one result or the other experimentally, e.g. by doing PCR > > with some of the genes in question. > > > > 2. Number of "core" genes: > > Please note that the number of "core" transcripts is defined in the > > Affymetrix probeset annotation file. Only probesets with "level=Core" > > are combined as "core" transcripts, and only the subset with > > "crosshyb_type=unique" is used as "metacore" transcripts. Thus the > > number of "core" transcripts depends on the annotation used. For the > > current Affymetrix probeset annotation file version > > "HuEx-1_0-st-v2.na28.hg18.probeset.csv" you get 18708 "core" > transcripts > > and 17880 "metacore" transcripts. Since the difference is 828 > > transcripts, I assume that you are only using the "metacore" > transcripts > > with Partek. Thus, in xps you need to use exonlevel="metacore". > > > > 3. Probes used for background correction: > > Package xps contains an internal function exonLevel() which is used to > > convert parameter "exonlevel" to an integer. Because of your question I > > have decided to make this function public in version xps_1.2.9, which > > you should be able to download on Monday. The corresponding helpfile > > "?exonLevel" will explain the different integers to be used. > > For your convenience the integers are: core (=8192+1024), extended > > (=4096+512), full (=2048+256), ambiguous (=128), affx(=60). Thus > > "core+extended+full" is 16128. > > > > Coincidently, the question which probes to be used for background > > correction was recently asked also at: > > > http://groups.google.com/group/aroma- affymetrix/browse_thread/thread/69fdc2757894f290# > > > > Best regards > > Christian > > > > > > arnaud Le Cavorzin wrote: > > > Dear Christian, > > > > > > Thank you for your answer. > > > > > > We have tried to perform rma normalization using > > > exonlevel=c(16316,9216,9216) that is to say using all of the probes > > > for the background correction and core probes for the two last steps. > > > And the results seems to be better. We found results closed to those > > > obtained with Partek, and after performing t.test and fdr correction > > > we found the same result with Partek and with xps : no differencially > > > expressed genes even if the p-values and fdr values are a slightly > > > different. > > > > > > In fact there is always 827 genes more with xps than with Partek, so > > > it is maybe the explanation of these differences. > > > So our question : Is it possible to perform rma using only > > > core+extended+full probes for the background correction? If yes what > > > is the "number" needs for this? (core => 9216, metacore => 8192, all > > > => 16316) > > > > > > Thank you very much > > > Best regards > > > > > > Arnaud > > > > > > > > > > Date: Wed, 1 Apr 2009 21:31:54 +0200 > > > > From: cstrato at aon.at > > > > To: arnaudlc at msn.com > > > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu-rennes.fr > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > Partek GS > > > > > > > > Dear Arnaud, > > > > > > > > Yes, xps uses the same background algorithm as APT (and Partek) but > > > uses > > > > by default only the probes defined in "exonlevel" for background > > > > correction and quantile normalization, i.e. in your case only the > > > "core" > > > > probes are used. > > > > > > > > However, xps offers the possibility to select the probes to be > used for > > > > background correction, quantile normalization and summarization > > > > individually. For RMA normalization you can do: > > > > > > > > data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, > > > > background="antigenomic", > > > > normalize=T, option="transcript", > > > > exonlevel=c(16316,16316,9216)) > > > > > > > > This means that probes "core+extended+full+ambiguous+affx" > (=16316) are > > > > used for background correction and quantile normalization, > > > respectively, > > > > and probes "core" (=9216) are used for summarization. > > > > > > > > As you will see, the results will be more similar to the results > > > > obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). > However, > > > > it is my belief that it is better to use the same probes for all > three > > > > steps. Maybe this is reflected in your results of finding > > > differentially > > > > expressed genes? > > > > > > > > Best regards > > > > Christian > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > Hi. > > > > > > > > > > Thank you for your anwser. > > > > > > > > > > I have downloaded the last version of xps and performed the > > > > > metaProbesets with success. > > > > > > > > > > > > > > > So if I understand, xps use the same background correction > algorithm > > > > > than Partek or APT. But the difference between these softwares > seems > > > > > to be caused by the probes used for the background correction. > > > > > Moreover it seems to get a difference not only for the background > > > > > correction, but for the quantile normalization too, again due > to the > > > > > different probes. > > > > > > > > > > In fact I can't get the same results on my samples with xps > package > > > > > and with Partek. > > > > > With Partek we found no expression differences for all of the > genes > > > > > (after fdr correction), while xps found a little more than > 1100 genes > > > > > who are differentially expressed (after using prefilter() and > > > > > unifilter() function, with the same options than in Partek). > > > > > > > > > > And I don't know why. > > > > > > > > > > Thank you for your help and your answer, > > > > > Best regards > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > > > > > From: cstrato at aon.at > > > > > > To: arnaudlc at msn.com > > > > > > CC: bioconductor at stat.math.ethz.ch; > delphine.rossille at chu-rennes.fr > > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > > Partek GS > > > > > > > > > > > > Dear Arnaud, > > > > > > > > > > > > Let me first answer your question how to export the > background data > > > > > > (although an example is shown in vignette xps.pdf p.15): > > > > > > > > > > > > # 1. compute background (or rma) > > > > > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", > filedir=datdir, > > > > > > method="rma", select="antigenomic", > option="pmonly:epanechnikov", > > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > > > # 2. find background treenames for data.bg > > > > > > getTreeNamesrootFiledata.bg)) > > > > > > > > > > > > # 3. get background for all trees > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > setNamedata.bg), > > > > > > "*", "rbg", "fBg", "BgrdAll.txt") > > > > > > # or get background intensity for e.g. tree "BreastA.rbg" > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > setNamedata.bg), > > > > > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > > > > > > > > > In addition you can also export the background subtracted > > > intensities: > > > > > > # 4. get background corrected intensities for all trees > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > setNamedata.bg), > > > > > > "*", "int", "fInten", "IntenAll.txt") > > > > > > > > > > > > However, please note that only the "core" probes are corrected, > > > so I am > > > > > > not sure if you can use these data in another program. > > > > > > > > > > > > > > > > > > Some notes on background correction: > > > > > > > > > > > > Please note that the above background correction does NOT use > > > > > > "antigenomic" probes for background correction. The parameter > > > > > > select="antigenomic" does only define the kind of MM probes > > > (although > > > > > > they are not used in this case). Which probes are used as PM > > > probes is > > > > > > defined by exonlevel="core", which means that only "core" > probes are > > > > > > used for background correction. > > > > > > > > > > > > As you know, in the RMA background algorithm observed PM > probes are > > > > > > modeled as the sum of a normal noise component and an > exponential > > > > > signal > > > > > > component. Since in above case only "core" probes are > selected as PM > > > > > > probes, only these probes are used for background correction. > > > This may > > > > > > be the reason why the background data differ from the > background > > > data > > > > > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > > > > > > > > > If you want to compute the background using "genomic" or > > > "antigenomic" > > > > > > probes and the APT algorithm based on GC content of these > probes > > > then > > > > > > you need to use: > > > > > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", > filedir=datdir, > > > > > > method="gccontent", select="antigenomic", option="attenuatebg", > > > > > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > > > > > or the dedicated function: > > > > > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", > filedir=datdir, > > > > > > select="antigenomic", exonlevel="core") > > > > > > > > > > > > > > > > > > Maybe one note on processing time: > > > > > > My main goal was to allow processing of exon arrays on > computers > > > with > > > > > > 1GB RAM only, and to allow access to all interim data such as > > > > > background > > > > > > intensities and background-corrected probe intensities. Thus, > > > all these > > > > > > data are stored as root trees, which means that saving all these > > > > > interim > > > > > > data on HD is probably the time-consuming step. > > > > > > > > > > > > I hope that I could answer your questions. > > > > > > > > > > > > Best regards > > > > > > Christian > > > > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > > Hi > > > > > > > > > > > > > > Thank you for your answer, I will download your last version > > > when it > > > > > > > will be available. > > > > > > > > > > > > > > We found that the difference between Partek GS and xps > package > > > is the > > > > > > > background correction. > > > > > > > And Partek's support confirmed that Partek GS doesn't use > > > genomic or > > > > > > > antigenomic background correction but correction like > described by > > > > > > > Professor Bolstad. > > > > > > > > > > > > > > But I have an other problem : I would like to perform > background > > > > > > > correction with xps, using the function bgcorrect(), and > after to > > > > > > > export the data.bg.rma for performing the normalization and > > > > > > > summarization with Partek GS (that take less time than xps) > > > > > > > I have tried with function export.expr(), export.data and > export() > > > > > > > without success. > > > > > > > > > > > > > > How can I do to extract the data.bg.rma into a .txt file, if > > > it was > > > > > > > possible of course? > > > > > > > > > > > > > > Thanks > > > > > > > Best regards > > > > > > > > > > > > > > Arnaud > > > > -------------------------------------------------------------------- ---- > Vous voulez savoir ce que vous pouvez faire avec le nouveau Windows > Live ? Lancez-vous ! > <http: www.microsoft.com="" windows="" windowslive="" default.aspx="">
ADD REPLY
0
Entering edit mode
Dear Christian Thanks for your answer. We have used the .mps file created with xps package with Partek, and the results are different too. I have compared xps package with Partek, APT, Expression Console and aroma.affymetrix and the results are always differents. So I think I will work with xps and with Partek both, because of the little differences that we found it will be interesting to compare these results. Thank you very much for your help, your patience and your reactivity. Best regards. Arnaud > Date: Mon, 6 Apr 2009 23:30:36 +0200 > From: cstrato@aon.at > To: arnaudlc@msn.com > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu-rennes.fr > Subject: Re: [BioC] Xps package : errors and RMA difference with Partek GS > > Dear Arnaud, > > As I said already in an earlier mail, when comparing xps and APT I get > slightly different expression levels, which may be due to different > probes used for background correction and quantile normalization since > the results obtained for median-polish only are identical (see vignette > APTvsXPS.pdf). For this reason the means are also slightly different, > and thus also the p-values. > > Furthermore, in order to compare xps and APT I needed to create a > metaprobeset file using function "metaProbesets()" which I used with > APT. This file assures that the same metacore probesets are used by both > programs. > > Please note that cannot comment on differences between xps and any > commercial software, I can only compare xps to other open-source > programs such as affy and APT. However, if you compare your software to > APT and the results are identical, then this would answer your question. > > Regarding fdr adjustment you will see that "validData(rma.ufr)" lists > only the probesets which satisfy the condition pval<0.05. In order to > see all probesets you need to do "validData(rma.ufr,"UnitName")" or > simply "rma.ufr@data". (I must admit that I need to document this > feature in the help file.) Then you should see also differences between > p-value and p-adjusted. But I need to investigate further. > > Best regards > Christian > > > arnaud Le Cavorzin wrote: > > Dear Christian, > > > > Thank you very much for your answer and for your reactivity. > > > > We have performed RMA normalization using exonlevel=c(16316,8192,8192) > > that is to say using all probes for the background correction and only > > metacore probes for the quantile normalization and summarization, so > > the same options using by Partek ("core" in Partek corresponding to > > "metacore" in xps, like you have suggested it). > > > > We get the same number of probeset with Partek and xps package, but > > the results are differents for the two softwares. > > Even if it was better, we found 3539 genes with a p-value<0.05 with > > xps, and 3337 genes with p-value<0.05 with Partek, and the results > > remain different for the two softwares. We don't obtain the same > > p-value, in particular because we don't obtain the same means. > > > > I have also imported the data.rma from xps to Partek, and performed > > the t test with Partek : the results are the same than performing > > unifilter with xps, we obtain the same p value than with xps and the > > same means. So they are still different than the results using Partek > > only. > > (Confirm that there is a difference with the probes used in Partek for > > the RMA normalization) > > > > Another question : when I use fdr correction or no correction with > > xps, the results are still the same. Only when I use bonferroni > > correction the p-adjusted change. I don't understand why FDR > > correction have no effect. > > > > /> > > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALS E,0.95,TRUE), > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet ",getwd(),logbase="log2", > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > > / > > /> > > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE ,0.95,TRUE), > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet ",getwd(),logbase="log2", > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > > > > Thanks > > Best regards, > > > > Arnaud > > > > > > > Date: Fri, 3 Apr 2009 20:25:15 +0200 > > > From: cstrato@aon.at > > > To: arnaudlc@msn.com > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > Partek GS > > > > > > Dear Arnaud, > > > > > > Let me answer your questions individually: > > > > > > 1. Differential expression: > > > As you see, package xps is quite flexible and allows to use different > > > probes for background correction, quantile normalization and > > > summarization. This may have effects on the results you get with the > > > different settings. Thus, the most important question to determine > > which > > > setting to choose, is: > > > Do you expect to find differentially expressed genes in your experiment > > > or not? > > > Since the different settings give different results the best way would > > > be to confirm one result or the other experimentally, e.g. by doing PCR > > > with some of the genes in question. > > > > > > 2. Number of "core" genes: > > > Please note that the number of "core" transcripts is defined in the > > > Affymetrix probeset annotation file. Only probesets with "level=Core" > > > are combined as "core" transcripts, and only the subset with > > > "crosshyb_type=unique" is used as "metacore" transcripts. Thus the > > > number of "core" transcripts depends on the annotation used. For the > > > current Affymetrix probeset annotation file version > > > "HuEx-1_0-st-v2.na28.hg18.probeset.csv" you get 18708 "core" > > transcripts > > > and 17880 "metacore" transcripts. Since the difference is 828 > > > transcripts, I assume that you are only using the "metacore" > > transcripts > > > with Partek. Thus, in xps you need to use exonlevel="metacore". > > > > > > 3. Probes used for background correction: > > > Package xps contains an internal function exonLevel() which is used to > > > convert parameter "exonlevel" to an integer. Because of your question I > > > have decided to make this function public in version xps_1.2.9, which > > > you should be able to download on Monday. The corresponding helpfile > > > "?exonLevel" will explain the different integers to be used. > > > For your convenience the integers are: core (=8192+1024), extended > > > (=4096+512), full (=2048+256), ambiguous (=128), affx(=60). Thus > > > "core+extended+full" is 16128. > > > > > > Coincidently, the question which probes to be used for background > > > correction was recently asked also at: > > > > > http://groups.google.com/group/aroma- affymetrix/browse_thread/thread/69fdc2757894f290# > > > > > > Best regards > > > Christian > > > > > > > > > arnaud Le Cavorzin wrote: > > > > Dear Christian, > > > > > > > > Thank you for your answer. > > > > > > > > We have tried to perform rma normalization using > > > > exonlevel=c(16316,9216,9216) that is to say using all of the probes > > > > for the background correction and core probes for the two last steps. > > > > And the results seems to be better. We found results closed to those > > > > obtained with Partek, and after performing t.test and fdr correction > > > > we found the same result with Partek and with xps : no differencially > > > > expressed genes even if the p-values and fdr values are a slightly > > > > different. > > > > > > > > In fact there is always 827 genes more with xps than with Partek, so > > > > it is maybe the explanation of these differences. > > > > So our question : Is it possible to perform rma using only > > > > core+extended+full probes for the background correction? If yes what > > > > is the "number" needs for this? (core => 9216, metacore => 8192, all > > > > => 16316) > > > > > > > > Thank you very much > > > > Best regards > > > > > > > > Arnaud > > > > > > > > > > > > > Date: Wed, 1 Apr 2009 21:31:54 +0200 > > > > > From: cstrato@aon.at > > > > > To: arnaudlc@msn.com > > > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > Partek GS > > > > > > > > > > Dear Arnaud, > > > > > > > > > > Yes, xps uses the same background algorithm as APT (and Partek) but > > > > uses > > > > > by default only the probes defined in "exonlevel" for background > > > > > correction and quantile normalization, i.e. in your case only the > > > > "core" > > > > > probes are used. > > > > > > > > > > However, xps offers the possibility to select the probes to be > > used for > > > > > background correction, quantile normalization and summarization > > > > > individually. For RMA normalization you can do: > > > > > > > > > > data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, > > > > > background="antigenomic", > > > > > normalize=T, option="transcript", > > > > > exonlevel=c(16316,16316,9216)) > > > > > > > > > > This means that probes "core+extended+full+ambiguous+affx" > > (=16316) are > > > > > used for background correction and quantile normalization, > > > > respectively, > > > > > and probes "core" (=9216) are used for summarization. > > > > > > > > > > As you will see, the results will be more similar to the results > > > > > obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). > > However, > > > > > it is my belief that it is better to use the same probes for all > > three > > > > > steps. Maybe this is reflected in your results of finding > > > > differentially > > > > > expressed genes? > > > > > > > > > > Best regards > > > > > Christian > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > Hi. > > > > > > > > > > > > Thank you for your anwser. > > > > > > > > > > > > I have downloaded the last version of xps and performed the > > > > > > metaProbesets with success. > > > > > > > > > > > > > > > > > > So if I understand, xps use the same background correction > > algorithm > > > > > > than Partek or APT. But the difference between these softwares > > seems > > > > > > to be caused by the probes used for the background correction. > > > > > > Moreover it seems to get a difference not only for the background > > > > > > correction, but for the quantile normalization too, again due > > to the > > > > > > different probes. > > > > > > > > > > > > In fact I can't get the same results on my samples with xps > > package > > > > > > and with Partek. > > > > > > With Partek we found no expression differences for all of the > > genes > > > > > > (after fdr correction), while xps found a little more than > > 1100 genes > > > > > > who are differentially expressed (after using prefilter() and > > > > > > unifilter() function, with the same options than in Partek). > > > > > > > > > > > > And I don't know why. > > > > > > > > > > > > Thank you for your help and your answer, > > > > > > Best regards > > > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > > > > > > From: cstrato@aon.at > > > > > > > To: arnaudlc@msn.com > > > > > > > CC: bioconductor@stat.math.ethz.ch; > > delphine.rossille@chu-rennes.fr > > > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > > > Partek GS > > > > > > > > > > > > > > Dear Arnaud, > > > > > > > > > > > > > > Let me first answer your question how to export the > > background data > > > > > > > (although an example is shown in vignette xps.pdf p.15): > > > > > > > > > > > > > > # 1. compute background (or rma) > > > > > > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", > > filedir=datdir, > > > > > > > method="rma", select="antigenomic", > > option="pmonly:epanechnikov", > > > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > > > > > # 2. find background treenames for data.bg > > > > > > > getTreeNamesrootFiledata.bg)) > > > > > > > > > > > > > > # 3. get background for all trees > > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > > setNamedata.bg), > > > > > > > "*", "rbg", "fBg", "BgrdAll.txt") > > > > > > > # or get background intensity for e.g. tree "BreastA.rbg" > > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > > setNamedata.bg), > > > > > > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > > > > > > > > > > > In addition you can also export the background subtracted > > > > intensities: > > > > > > > # 4. get background corrected intensities for all trees > > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > > setNamedata.bg), > > > > > > > "*", "int", "fInten", "IntenAll.txt") > > > > > > > > > > > > > > However, please note that only the "core" probes are corrected, > > > > so I am > > > > > > > not sure if you can use these data in another program. > > > > > > > > > > > > > > > > > > > > > Some notes on background correction: > > > > > > > > > > > > > > Please note that the above background correction does NOT use > > > > > > > "antigenomic" probes for background correction. The parameter > > > > > > > select="antigenomic" does only define the kind of MM probes > > > > (although > > > > > > > they are not used in this case). Which probes are used as PM > > > > probes is > > > > > > > defined by exonlevel="core", which means that only "core" > > probes are > > > > > > > used for background correction. > > > > > > > > > > > > > > As you know, in the RMA background algorithm observed PM > > probes are > > > > > > > modeled as the sum of a normal noise component and an > > exponential > > > > > > signal > > > > > > > component. Since in above case only "core" probes are > > selected as PM > > > > > > > probes, only these probes are used for background correction. > > > > This may > > > > > > > be the reason why the background data differ from the > > background > > > > data > > > > > > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > > > > > > > > > > > If you want to compute the background using "genomic" or > > > > "antigenomic" > > > > > > > probes and the APT algorithm based on GC content of these > > probes > > > > then > > > > > > > you need to use: > > > > > > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", > > filedir=datdir, > > > > > > > method="gccontent", select="antigenomic", option="attenuatebg", > > > > > > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > > > > > > or the dedicated function: > > > > > > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", > > filedir=datdir, > > > > > > > select="antigenomic", exonlevel="core") > > > > > > > > > > > > > > > > > > > > > Maybe one note on processing time: > > > > > > > My main goal was to allow processing of exon arrays on > > computers > > > > with > > > > > > > 1GB RAM only, and to allow access to all interim data such as > > > > > > background > > > > > > > intensities and background-corrected probe intensities. Thus, > > > > all these > > > > > > > data are stored as root trees, which means that saving all these > > > > > > interim > > > > > > > data on HD is probably the time-consuming step. > > > > > > > > > > > > > > I hope that I could answer your questions. > > > > > > > > > > > > > > Best regards > > > > > > > Christian > > > > > > > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > > > Hi > > > > > > > > > > > > > > > > Thank you for your answer, I will download your last version > > > > when it > > > > > > > > will be available. > > > > > > > > > > > > > > > > We found that the difference between Partek GS and xps > > package > > > > is the > > > > > > > > background correction. > > > > > > > > And Partek's support confirmed that Partek GS doesn't use > > > > genomic or > > > > > > > > antigenomic background correction but correction like > > described by > > > > > > > > Professor Bolstad. > > > > > > > > > > > > > > > > But I have an other problem : I would like to perform > > background > > > > > > > > correction with xps, using the function bgcorrect(), and > > after to > > > > > > > > export the data.bg.rma for performing the normalization and > > > > > > > > summarization with Partek GS (that take less time than xps) > > > > > > > > I have tried with function export.expr(), export.data and > > export() > > > > > > > > without success. > > > > > > > > > > > > > > > > How can I do to extract the data.bg.rma into a .txt file, if > > > > it was > > > > > > > > possible of course? > > > > > > > > > > > > > > > > Thanks > > > > > > > > Best regards > > > > > > > > > > > > > > > > Arnaud > > > > > > > ------------------------------------------------------------------ ------ > > Vous voulez savoir ce que vous pouvez faire avec le nouveau Windows > > Live ? Lancez-vous ! > > <http: www.microsoft.com="" windows="" windowslive="" default.aspx=""> > _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]
ADD REPLY
0
Entering edit mode
Dear Arnaud, I agree with you that it is best to compare the results. Your finding that the different software packages give slightly different results is expected for HuExon arrays. If you want to test if the RMA algorithm is implemented correctly, you need to use data from expression arrays, e.g. HG-U133_Plus_2. I have done this for xps, affy and APT and the results were identical, as shown in Fig.3 of vignette APTvsXPS.pdf. Best regards Christian arnaud Le Cavorzin wrote: > Dear Christian > > Thanks for your answer. > > We have used the .mps file created with xps package with Partek, and > the results are different too. > > I have compared xps package with Partek, APT, Expression Console and > aroma.affymetrix and the results are always differents. > > So I think I will work with xps and with Partek both, because of the > little differences that we found it will be interesting to compare > these results. > > Thank you very much for your help, your patience and your reactivity. > Best regards. > > Arnaud > > > Date: Mon, 6 Apr 2009 23:30:36 +0200 > > From: cstrato at aon.at > > To: arnaudlc at msn.com > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu- rennes.fr > > Subject: Re: [BioC] Xps package : errors and RMA difference with > Partek GS > > > > Dear Arnaud, > > > > As I said already in an earlier mail, when comparing xps and APT I get > > slightly different expression levels, which may be due to different > > probes used for background correction and quantile normalization since > > the results obtained for median-polish only are identical (see vignette > > APTvsXPS.pdf). For this reason the means are also slightly different, > > and thus also the p-values. > > > > Furthermore, in order to compare xps and APT I needed to create a > > metaprobeset file using function "metaProbesets()" which I used with > > APT. This file assures that the same metacore probesets are used by > both > > programs. > > > > Please note that cannot comment on differences between xps and any > > commercial software, I can only compare xps to other open-source > > programs such as affy and APT. However, if you compare your software to > > APT and the results are identical, then this would answer your question. > > > > Regarding fdr adjustment you will see that "validData(rma.ufr)" lists > > only the probesets which satisfy the condition pval<0.05. In order to > > see all probesets you need to do "validData(rma.ufr,"UnitName")" or > > simply "rma.ufr at data". (I must admit that I need to document this > > feature in the help file.) Then you should see also differences between > > p-value and p-adjusted. But I need to investigate further. > > > > Best regards > > Christian > > > > > > arnaud Le Cavorzin wrote: > > > Dear Christian, > > > > > > Thank you very much for your answer and for your reactivity. > > > > > > We have performed RMA normalization using > exonlevel=c(16316,8192,8192) > > > that is to say using all probes for the background correction and > only > > > metacore probes for the quantile normalization and summarization, so > > > the same options using by Partek ("core" in Partek corresponding to > > > "metacore" in xps, like you have suggested it). > > > > > > We get the same number of probeset with Partek and xps package, but > > > the results are differents for the two softwares. > > > Even if it was better, we found 3539 genes with a p-value<0.05 with > > > xps, and 3337 genes with p-value<0.05 with Partek, and the results > > > remain different for the two softwares. We don't obtain the same > > > p-value, in particular because we don't obtain the same means. > > > > > > I have also imported the data.rma from xps to Partek, and performed > > > the t test with Partek : the results are the same than performing > > > unifilter with xps, we obtain the same p value than with xps and the > > > same means. So they are still different than the results using Partek > > > only. > > > (Confirm that there is a difference with the probes used in Partek > for > > > the RMA normalization) > > > > > > Another question : when I use fdr correction or no correction with > > > xps, the results are still the same. Only when I use bonferroni > > > correction the p-adjusted change. I don't understand why FDR > > > correction have no effect. > > > > > > /> > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALSE, 0.95,TRUE), > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > > > / > > > /> > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE,0 .95,TRUE), > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > > > > > > > Thanks > > > Best regards, > > > > > > Arnaud > > > > > > > > > > Date: Fri, 3 Apr 2009 20:25:15 +0200 > > > > From: cstrato at aon.at > > > > To: arnaudlc at msn.com > > > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu-rennes.fr > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > Partek GS > > > > > > > > Dear Arnaud, > > > > > > > > Let me answer your questions individually: > > > > > > > > 1. Differential expression: > > > > As you see, package xps is quite flexible and allows to use > different > > > > probes for background correction, quantile normalization and > > > > summarization. This may have effects on the results you get with the > > > > different settings. Thus, the most important question to determine > > > which > > > > setting to choose, is: > > > > Do you expect to find differentially expressed genes in your > experiment > > > > or not? > > > > Since the different settings give different results the best way > would > > > > be to confirm one result or the other experimentally, e.g. by > doing PCR > > > > with some of the genes in question. > > > > > > > > 2. Number of "core" genes: > > > > Please note that the number of "core" transcripts is defined in the > > > > Affymetrix probeset annotation file. Only probesets with > "level=Core" > > > > are combined as "core" transcripts, and only the subset with > > > > "crosshyb_type=unique" is used as "metacore" transcripts. Thus the > > > > number of "core" transcripts depends on the annotation used. For the > > > > current Affymetrix probeset annotation file version > > > > "HuEx-1_0-st-v2.na28.hg18.probeset.csv" you get 18708 "core" > > > transcripts > > > > and 17880 "metacore" transcripts. Since the difference is 828 > > > > transcripts, I assume that you are only using the "metacore" > > > transcripts > > > > with Partek. Thus, in xps you need to use exonlevel="metacore". > > > > > > > > 3. Probes used for background correction: > > > > Package xps contains an internal function exonLevel() which is > used to > > > > convert parameter "exonlevel" to an integer. Because of your > question I > > > > have decided to make this function public in version xps_1.2.9, > which > > > > you should be able to download on Monday. The corresponding helpfile > > > > "?exonLevel" will explain the different integers to be used. > > > > For your convenience the integers are: core (=8192+1024), extended > > > > (=4096+512), full (=2048+256), ambiguous (=128), affx(=60). Thus > > > > "core+extended+full" is 16128. > > > > > > > > Coincidently, the question which probes to be used for background > > > > correction was recently asked also at: > > > > > > > > http://groups.google.com/group/aroma- affymetrix/browse_thread/thread/69fdc2757894f290# > > > > > > > > Best regards > > > > Christian > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > Dear Christian, > > > > > > > > > > Thank you for your answer. > > > > > > > > > > We have tried to perform rma normalization using > > > > > exonlevel=c(16316,9216,9216) that is to say using all of the > probes > > > > > for the background correction and core probes for the two last > steps. > > > > > And the results seems to be better. We found results closed to > those > > > > > obtained with Partek, and after performing t.test and fdr > correction > > > > > we found the same result with Partek and with xps : no > differencially > > > > > expressed genes even if the p-values and fdr values are a slightly > > > > > different. > > > > > > > > > > In fact there is always 827 genes more with xps than with > Partek, so > > > > > it is maybe the explanation of these differences. > > > > > So our question : Is it possible to perform rma using only > > > > > core+extended+full probes for the background correction? If > yes what > > > > > is the "number" needs for this? (core => 9216, metacore => > 8192, all > > > > > => 16316) > > > > > > > > > > Thank you very much > > > > > Best regards > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > Date: Wed, 1 Apr 2009 21:31:54 +0200 > > > > > > From: cstrato at aon.at > > > > > > To: arnaudlc at msn.com > > > > > > CC: bioconductor at stat.math.ethz.ch; > delphine.rossille at chu-rennes.fr > > > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > > > Partek GS > > > > > > > > > > > > Dear Arnaud, > > > > > > > > > > > > Yes, xps uses the same background algorithm as APT (and > Partek) but > > > > > uses > > > > > > by default only the probes defined in "exonlevel" for background > > > > > > correction and quantile normalization, i.e. in your case > only the > > > > > "core" > > > > > > probes are used. > > > > > > > > > > > > However, xps offers the possibility to select the probes to be > > > used for > > > > > > background correction, quantile normalization and summarization > > > > > > individually. For RMA normalization you can do: > > > > > > > > > > > > data.rma <- rma(data.exon, "ExonRMAbq16core", filedir=datdir, > > > > > > background="antigenomic", > > > > > > normalize=T, option="transcript", > > > > > > exonlevel=c(16316,16316,9216)) > > > > > > > > > > > > This means that probes "core+extended+full+ambiguous+affx" > > > (=16316) are > > > > > > used for background correction and quantile normalization, > > > > > respectively, > > > > > > and probes "core" (=9216) are used for summarization. > > > > > > > > > > > > As you will see, the results will be more similar to the results > > > > > > obtained with APT (see also Figures 17 vs 15 in APTvsXPS.pdf). > > > However, > > > > > > it is my belief that it is better to use the same probes for > all > > > three > > > > > > steps. Maybe this is reflected in your results of finding > > > > > differentially > > > > > > expressed genes? > > > > > > > > > > > > Best regards > > > > > > Christian > > > > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > > Hi. > > > > > > > > > > > > > > Thank you for your anwser. > > > > > > > > > > > > > > I have downloaded the last version of xps and performed the > > > > > > > metaProbesets with success. > > > > > > > > > > > > > > > > > > > > > So if I understand, xps use the same background correction > > > algorithm > > > > > > > than Partek or APT. But the difference between these > softwares > > > seems > > > > > > > to be caused by the probes used for the background correction. > > > > > > > Moreover it seems to get a difference not only for the > background > > > > > > > correction, but for the quantile normalization too, again due > > > to the > > > > > > > different probes. > > > > > > > > > > > > > > In fact I can't get the same results on my samples with xps > > > package > > > > > > > and with Partek. > > > > > > > With Partek we found no expression differences for all of the > > > genes > > > > > > > (after fdr correction), while xps found a little more than > > > 1100 genes > > > > > > > who are differentially expressed (after using prefilter() and > > > > > > > unifilter() function, with the same options than in Partek). > > > > > > > > > > > > > > And I don't know why. > > > > > > > > > > > > > > Thank you for your help and your answer, > > > > > > > Best regards > > > > > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > > > > > > > Date: Tue, 31 Mar 2009 22:04:42 +0200 > > > > > > > > From: cstrato at aon.at > > > > > > > > To: arnaudlc at msn.com > > > > > > > > CC: bioconductor at stat.math.ethz.ch; > > > delphine.rossille at chu-rennes.fr > > > > > > > > Subject: Re: [BioC] Xps package : errors and RMA > difference with > > > > > > > Partek GS > > > > > > > > > > > > > > > > Dear Arnaud, > > > > > > > > > > > > > > > > Let me first answer your question how to export the > > > background data > > > > > > > > (although an example is shown in vignette xps.pdf p.15): > > > > > > > > > > > > > > > > # 1. compute background (or rma) > > > > > > > > data.bg <- bgcorrect(data.exon, "ExonRMABgrdCore", > > > filedir=datdir, > > > > > > > > method="rma", select="antigenomic", > > > option="pmonly:epanechnikov", > > > > > > > > params=c(16384), exonlevel="core") > > > > > > > > > > > > > > > > # 2. find background treenames for data.bg > > > > > > > > getTreeNamesrootFiledata.bg)) > > > > > > > > > > > > > > > > # 3. get background for all trees > > > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > > > setNamedata.bg), > > > > > > > > "*", "rbg", "fBg", "BgrdAll.txt") > > > > > > > > # or get background intensity for e.g. tree "BreastA.rbg" > > > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > > > setNamedata.bg), > > > > > > > > "BreastA", "rbg", "fBg", "BgrdBreastA.txt") > > > > > > > > > > > > > > > > In addition you can also export the background subtracted > > > > > intensities: > > > > > > > > # 4. get background corrected intensities for all trees > > > > > > > > export.rootrootFiledata.bg), schemeFiledata.bg), > > > > > setNamedata.bg), > > > > > > > > "*", "int", "fInten", "IntenAll.txt") > > > > > > > > > > > > > > > > However, please note that only the "core" probes are > corrected, > > > > > so I am > > > > > > > > not sure if you can use these data in another program. > > > > > > > > > > > > > > > > > > > > > > > > Some notes on background correction: > > > > > > > > > > > > > > > > Please note that the above background correction does > NOT use > > > > > > > > "antigenomic" probes for background correction. The > parameter > > > > > > > > select="antigenomic" does only define the kind of MM probes > > > > > (although > > > > > > > > they are not used in this case). Which probes are used as PM > > > > > probes is > > > > > > > > defined by exonlevel="core", which means that only "core" > > > probes are > > > > > > > > used for background correction. > > > > > > > > > > > > > > > > As you know, in the RMA background algorithm observed PM > > > probes are > > > > > > > > modeled as the sum of a normal noise component and an > > > exponential > > > > > > > signal > > > > > > > > component. Since in above case only "core" probes are > > > selected as PM > > > > > > > > probes, only these probes are used for background > correction. > > > > > This may > > > > > > > > be the reason why the background data differ from the > > > background > > > > > data > > > > > > > > computed by APT, as explained in vignette APTvsXPS.pdf. > > > > > > > > > > > > > > > > If you want to compute the background using "genomic" or > > > > > "antigenomic" > > > > > > > > probes and the APT algorithm based on GC content of these > > > probes > > > > > then > > > > > > > > you need to use: > > > > > > > > data.bg <- bgcorrect(data.exon, "ExonGCBgrdCore", > > > filedir=datdir, > > > > > > > > method="gccontent", select="antigenomic", > option="attenuatebg", > > > > > > > > params=c(0.4, 0.005, -1.0), exonlevel="core") > > > > > > > > or the dedicated function: > > > > > > > > data.bg <- bgcorrect.gc(data.exon, "ExonGCBgrdCore", > > > filedir=datdir, > > > > > > > > select="antigenomic", exonlevel="core") > > > > > > > > > > > > > > > > > > > > > > > > Maybe one note on processing time: > > > > > > > > My main goal was to allow processing of exon arrays on > > > computers > > > > > with > > > > > > > > 1GB RAM only, and to allow access to all interim data > such as > > > > > > > background > > > > > > > > intensities and background-corrected probe intensities. > Thus, > > > > > all these > > > > > > > > data are stored as root trees, which means that saving > all these > > > > > > > interim > > > > > > > > data on HD is probably the time-consuming step. > > > > > > > > > > > > > > > > I hope that I could answer your questions. > > > > > > > > > > > > > > > > Best regards > > > > > > > > Christian > > > > > > > > > > > > > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > > > > > Hi > > > > > > > > > > > > > > > > > > Thank you for your answer, I will download your last > version > > > > > when it > > > > > > > > > will be available. > > > > > > > > > > > > > > > > > > We found that the difference between Partek GS and xps > > > package > > > > > is the > > > > > > > > > background correction. > > > > > > > > > And Partek's support confirmed that Partek GS doesn't use > > > > > genomic or > > > > > > > > > antigenomic background correction but correction like > > > described by > > > > > > > > > Professor Bolstad. > > > > > > > > > > > > > > > > > > But I have an other problem : I would like to perform > > > background > > > > > > > > > correction with xps, using the function bgcorrect(), and > > > after to > > > > > > > > > export the data.bg.rma for performing the > normalization and > > > > > > > > > summarization with Partek GS (that take less time than > xps) > > > > > > > > > I have tried with function export.expr(), export.data and > > > export() > > > > > > > > > without success. > > > > > > > > > > > > > > > > > > How can I do to extract the data.bg.rma into a .txt > file, if > > > > > it was > > > > > > > > > possible of course? > > > > > > > > > > > > > > > > > > Thanks > > > > > > > > > Best regards > > > > > > > > > > > > > > > > > > Arnaud > > > > > > > > > > > -------------------------------------------------------------------- ---- > > > Vous voulez savoir ce que vous pouvez faire avec le nouveau Windows > > > Live ? Lancez-vous ! > > > <http: www.microsoft.com="" windows="" windowslive="" default.aspx=""> > > > > -------------------------------------------------------------------- ---- > D?couvrez tout ce que Windows Live a ? vous apporter ! > <http: www.microsoft.com="" windows="" windowslive=""/>
ADD REPLY
0
Entering edit mode
Dear Arnaud, I have just uploaded a new version xps_1.2.10 (and xps_1.3.12), which corrects the problem with fdr adjustment. Furthermore, I have added option "BY" (see ?uniTest) and crosschecked the results against R-function "p.adjust", thus the results should now be ok. Thank you for reporting this problem. Best regards Christian arnaud Le Cavorzin wrote: > Dear Christian, > > Thank you very much for your answer and for your reactivity. > > We have performed RMA normalization using exonlevel=c(16316,8192,8192) > that is to say using all probes for the background correction and only > metacore probes for the quantile normalization and summarization, so > the same options using by Partek ("core" in Partek corresponding to > "metacore" in xps, like you have suggested it). > > We get the same number of probeset with Partek and xps package, but > the results are differents for the two softwares. > Even if it was better, we found 3539 genes with a p-value<0.05 with > xps, and 3337 genes with p-value<0.05 with Partek, and the results > remain different for the two softwares. We don't obtain the same > p-value, in particular because we don't obtain the same means. > > I have also imported the data.rma from xps to Partek, and performed > the t test with Partek : the results are the same than performing > unifilter with xps, we obtain the same p value than with xps and the > same means. So they are still different than the results using Partek > only. > (Confirm that there is a difference with the probes used in Partek for > the RMA normalization) > > Another question : when I use fdr correction or no correction with > xps, the results are still the same. Only when I use bonferroni > correction the p-adjusted change. I don't understand why FDR > correction have no effect. > > /> > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALSE, 0.95,TRUE), > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > / > /> > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE,0 .95,TRUE), > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > Thanks > Best regards, > > Arnaud >
ADD REPLY
0
Entering edit mode
An embedded and charset-unspecified text was scrubbed... Name: not available URL: <https: stat.ethz.ch="" pipermail="" bioconductor="" attachments="" 20090416="" 68cdc879="" attachment.asc="">
ADD REPLY
0
Entering edit mode
Dear Arnaud, As you might know, in few days BioC 2.4 will become the new release version. For this version, packages are built using the new root production version root_5.22.00. Thus it seems that the servers are already updated to use root_5.22.00, which means that for xps_1.2.10 you need to install root_5.22.00. Best regards Christian arnaud Le Cavorzin wrote: > Dear Christian > > Thank you for your answer and your reactivity. > I have donwloaded the last version of xps but it doesn't work. When I > want to load the package I got an error : > > > library(xps) > Error in inDL(x, as.logical(local), as.logical(now), ...) : > impossible de charger la biblioth?que partag?e > 'C:/PROGRA~1/R/R-28~1.1/library/xps/libs/xps.dll': > LoadLibrary failure: La proc?dure sp?cifi?e est introuvable. > > > Erreur : le chargement du package / espace de noms a ?chou? pour 'xps' > > With an error window that say > ?GetClass at TClass@@SAPAV1 at ABVtype_info@@_N1 at Z is not find in libCore.dll. > > Thanks > Best regards > > Arnaud > > > Date: Sun, 12 Apr 2009 19:42:13 +0200 > > From: cstrato at aon.at > > To: arnaudlc at msn.com > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu- rennes.fr > > Subject: Re: [BioC] Xps package : errors and RMA difference with > Partek GS > > > > Dear Arnaud, > > > > I have just uploaded a new version xps_1.2.10 (and xps_1.3.12), which > > corrects the problem with fdr adjustment. Furthermore, I have added > > option "BY" (see ?uniTest) and crosschecked the results against > > R-function "p.adjust", thus the results should now be ok. > > > > Thank you for reporting this problem. > > Best regards > > Christian > > > > > > arnaud Le Cavorzin wrote: > > > Dear Christian, > > > > > > Thank you very much for your answer and for your reactivity. > > > > > > We have performed RMA normalization using > exonlevel=c(16316,8192,8192) > > > that is to say using all probes for the background correction and > only > > > metacore probes for the quantile normalization and summarization, so > > > the same options using by Partek ("core" in Partek corresponding to > > > "metacore" in xps, like you have suggested it). > > > > > > We get the same number of probeset with Partek and xps package, but > > > the results are differents for the two softwares. > > > Even if it was better, we found 3539 genes with a p-value<0.05 with > > > xps, and 3337 genes with p-value<0.05 with Partek, and the results > > > remain different for the two softwares. We don't obtain the same > > > p-value, in particular because we don't obtain the same means. > > > > > > I have also imported the data.rma from xps to Partek, and performed > > > the t test with Partek : the results are the same than performing > > > unifilter with xps, we obtain the same p value than with xps and the > > > same means. So they are still different than the results using Partek > > > only. > > > (Confirm that there is a difference with the probes used in Partek > for > > > the RMA normalization) > > > > > > Another question : when I use fdr correction or no correction with > > > xps, the results are still the same. Only when I use bonferroni > > > correction the p-adjusted change. I don't understand why FDR > > > correction have no effect. > > > > > > /> > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALSE, 0.95,TRUE), > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > > > / > > > /> > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE,0 .95,TRUE), > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > > > > > > > Thanks > > > Best regards, > > > > > > Arnaud > > > > > > > -------------------------------------------------------------------- ---- > Tous vos amis discutent sur Messenger, et vous ? T?l?chargez > Messenger, c'est gratuit ! <http: get.live.com="" messenger="" overview="">
ADD REPLY
0
Entering edit mode
Hi I'm sorry for this, I have not saw that before so I have not updated root. Now all is ok. Thank you for your answer, I will try next week the FDR correction. Best regards Arnaud > Date: Thu, 16 Apr 2009 21:08:32 +0200 > From: cstrato@aon.at > To: arnaudlc@msn.com > CC: bioconductor@stat.math.ethz.ch > Subject: Re: [BioC] Xps package : errors and RMA difference with Partek GS > > Dear Arnaud, > > As you might know, in few days BioC 2.4 will become the new release > version. For this version, packages are built using the new root > production version root_5.22.00. > > Thus it seems that the servers are already updated to use root_5.22.00, > which means that for xps_1.2.10 you need to install root_5.22.00. > > Best regards > Christian > > > arnaud Le Cavorzin wrote: > > Dear Christian > > > > Thank you for your answer and your reactivity. > > I have donwloaded the last version of xps but it doesn't work. When I > > want to load the package I got an error : > > > > > library(xps) > > Error in inDL(x, as.logical(local), as.logical(now), ...) : > > impossible de charger la bibliothèque partagée > > 'C:/PROGRA~1/R/R-28~1.1/library/xps/libs/xps.dll': > > LoadLibrary failure: La procédure spécifiée est introuvable. > > > > > > Erreur : le chargement du package / espace de noms a échoué pour 'xps' > > > > With an error window that say > > ?GetClass@TClass@@SAPAV1@ABVtype_info@@_N1@Z is not find in libCore.dll. > > > > Thanks > > Best regards > > > > Arnaud > > > > > Date: Sun, 12 Apr 2009 19:42:13 +0200 > > > From: cstrato@aon.at > > > To: arnaudlc@msn.com > > > CC: bioconductor@stat.math.ethz.ch; delphine.rossille@chu- rennes.fr > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > Partek GS > > > > > > Dear Arnaud, > > > > > > I have just uploaded a new version xps_1.2.10 (and xps_1.3.12), which > > > corrects the problem with fdr adjustment. Furthermore, I have added > > > option "BY" (see ?uniTest) and crosschecked the results against > > > R-function "p.adjust", thus the results should now be ok. > > > > > > Thank you for reporting this problem. > > > Best regards > > > Christian > > > > > > > > > arnaud Le Cavorzin wrote: > > > > Dear Christian, > > > > > > > > Thank you very much for your answer and for your reactivity. > > > > > > > > We have performed RMA normalization using > > exonlevel=c(16316,8192,8192) > > > > that is to say using all probes for the background correction and > > only > > > > metacore probes for the quantile normalization and summarization, so > > > > the same options using by Partek ("core" in Partek corresponding to > > > > "metacore" in xps, like you have suggested it). > > > > > > > > We get the same number of probeset with Partek and xps package, but > > > > the results are differents for the two softwares. > > > > Even if it was better, we found 3539 genes with a p-value<0.05 with > > > > xps, and 3337 genes with p-value<0.05 with Partek, and the results > > > > remain different for the two softwares. We don't obtain the same > > > > p-value, in particular because we don't obtain the same means. > > > > > > > > I have also imported the data.rma from xps to Partek, and performed > > > > the t test with Partek : the results are the same than performing > > > > unifilter with xps, we obtain the same p value than with xps and the > > > > same means. So they are still different than the results using Partek > > > > only. > > > > (Confirm that there is a difference with the probes used in Partek > > for > > > > the RMA normalization) > > > > > > > > Another question : when I use fdr correction or no correction with > > > > xps, the results are still the same. Only when I use bonferroni > > > > correction the p-adjusted change. I don't understand why FDR > > > > correction have no effect. > > > > > > > > /> > > > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALS E,0.95,TRUE), > > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet ",getwd(),logbase="log2", > > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > > > > / > > > > /> > > > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE ,0.95,TRUE), > > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet ",getwd(),logbase="log2", > > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > > > > > > > > > > Thanks > > > > Best regards, > > > > > > > > Arnaud > > > > > > > > > > > ------------------------------------------------------------------ ------ > > Tous vos amis discutent sur Messenger, et vous ? Téléchargez > > Messenger, c'est gratuit ! <http: get.live.com="" messenger="" overview=""> > _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]
ADD REPLY
0
Entering edit mode
Dear Arnaud I am glad to hear that the update of root solved your problem. I am sorry for the inconvenience. In order for users of xps to know which version of root to install, I will mention the root version in the future on the xps download site in System Requirements. Best regards Christian arnaud Le Cavorzin wrote: > Hi > > I'm sorry for this, I have not saw that before so I have not updated root. > Now all is ok. > > Thank you for your answer, I will try next week the FDR correction. > > Best regards > > Arnaud > > > Date: Thu, 16 Apr 2009 21:08:32 +0200 > > From: cstrato at aon.at > > To: arnaudlc at msn.com > > CC: bioconductor at stat.math.ethz.ch > > Subject: Re: [BioC] Xps package : errors and RMA difference with > Partek GS > > > > Dear Arnaud, > > > > As you might know, in few days BioC 2.4 will become the new release > > version. For this version, packages are built using the new root > > production version root_5.22.00. > > > > Thus it seems that the servers are already updated to use root_5.22.00, > > which means that for xps_1.2.10 you need to install root_5.22.00. > > > > Best regards > > Christian > > > > > > arnaud Le Cavorzin wrote: > > > Dear Christian > > > > > > Thank you for your answer and your reactivity. > > > I have donwloaded the last version of xps but it doesn't work. When I > > > want to load the package I got an error : > > > > > > > library(xps) > > > Error in inDL(x, as.logical(local), as.logical(now), ...) : > > > impossible de charger la biblioth?que partag?e > > > 'C:/PROGRA~1/R/R-28~1.1/library/xps/libs/xps.dll': > > > LoadLibrary failure: La proc?dure sp?cifi?e est introuvable. > > > > > > > > > Erreur : le chargement du package / espace de noms a ?chou? pour 'xps' > > > > > > With an error window that say > > > ?GetClass at TClass@@SAPAV1 at ABVtype_info@@_N1 at Z is not find in > libCore.dll. > > > > > > Thanks > > > Best regards > > > > > > Arnaud > > > > > > > Date: Sun, 12 Apr 2009 19:42:13 +0200 > > > > From: cstrato at aon.at > > > > To: arnaudlc at msn.com > > > > CC: bioconductor at stat.math.ethz.ch; delphine.rossille at chu-rennes.fr > > > > Subject: Re: [BioC] Xps package : errors and RMA difference with > > > Partek GS > > > > > > > > Dear Arnaud, > > > > > > > > I have just uploaded a new version xps_1.2.10 (and xps_1.3.12), > which > > > > corrects the problem with fdr adjustment. Furthermore, I have added > > > > option "BY" (see ?uniTest) and crosschecked the results against > > > > R-function "p.adjust", thus the results should now be ok. > > > > > > > > Thank you for reporting this problem. > > > > Best regards > > > > Christian > > > > > > > > > > > > arnaud Le Cavorzin wrote: > > > > > Dear Christian, > > > > > > > > > > Thank you very much for your answer and for your reactivity. > > > > > > > > > > We have performed RMA normalization using > > > exonlevel=c(16316,8192,8192) > > > > > that is to say using all probes for the background correction and > > > only > > > > > metacore probes for the quantile normalization and > summarization, so > > > > > the same options using by Partek ("core" in Partek > corresponding to > > > > > "metacore" in xps, like you have suggested it). > > > > > > > > > > We get the same number of probeset with Partek and xps > package, but > > > > > the results are differents for the two softwares. > > > > > Even if it was better, we found 3539 genes with a p-value<0.05 > with > > > > > xps, and 3337 genes with p-value<0.05 with Partek, and the results > > > > > remain different for the two softwares. We don't obtain the same > > > > > p-value, in particular because we don't obtain the same means. > > > > > > > > > > I have also imported the data.rma from xps to Partek, and > performed > > > > > the t test with Partek : the results are the same than performing > > > > > unifilter with xps, we obtain the same p value than with xps > and the > > > > > same means. So they are still different than the results using > Partek > > > > > only. > > > > > (Confirm that there is a difference with the probes used in > Partek > > > for > > > > > the RMA normalization) > > > > > > > > > > Another question : when I use fdr correction or no correction with > > > > > xps, the results are still the same. Only when I use bonferroni > > > > > correction the p-adjusted change. I don't understand why FDR > > > > > correction have no effect. > > > > > > > > > > /> > > > > > > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","none",0,0.0,FALSE, 0.95,TRUE), > > > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > > > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE) > > > > > / > > > > > /> > > > > > > > > > unifltr=UniFilter(unitest=c("t.test","two.sided","fdr",0,0.0,FALSE,0 .95,TRUE), > > > > > + foldchange=c(1,"both"),unifilter=c(0.05,"pval")) > > > > > > > > > > > > > > > rma.ufr=unifilter(data.test2.rma,"tmpdt_HuextestUnifilterallmetmet", getwd(),logbase="log2", > > > > > + unifltr,group=c("075","075","TEM","TEM"),verbose=FALSE)/ > > > > > > > > > > > > > > > Thanks > > > > > Best regards, > > > > > > > > > > Arnaud > > > > > > > > > > > > > > > > -------------------------------------------------------------------- ---- > > > Tous vos amis discutent sur Messenger, et vous ? T?l?chargez > > > Messenger, c'est gratuit ! <http: get.live.com="" messenger="" overview=""> > > > > -------------------------------------------------------------------- ---- > D?couvrez tout ce que Windows Live a ? vous apporter ! > <http: www.microsoft.com="" windows="" windowslive=""/>
ADD REPLY

Login before adding your answer.

Traffic: 299 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6