Normalization of bi-modal expression data
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@christian-briere-3259
Last seen 10.2 years ago
Hi! I am new in microarray analysis and in using Bioconductor. I need to analyse expression data from monocolor Agilent microarrays (105K). To my surprise, for each array (controls as well as treated samples) the distribution of intensity data is bi-modal. Furthermore, it seems that more than 10% of the genes are differentially expressed between controls and treated samples. Therefore, I wonder what is the best method to use in such case for between arrays normalization. I was told that median or quantile normalization was not adequate. Should Invariant Set or VSN normalization be better, and what are the packages to use for that ? Thanks for your help -- Christian Brière UMR CNRS-UPS 5546 BP42617 Auzeville F-31326 Castanet-Tolosan (France) tel: +33(0)5 62 19 35 90 Fax: +33(0)5 62 19 35 02 E-mail: briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-tlse.fr> http://www.scsv.ups-tlse.fr http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> http://www.ifr40.cnrs.fr [[alternative HTML version deleted]]
Microarray Normalization Microarray Normalization • 1.9k views
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 3.6 years ago
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Did you remove the Agilent controls before looking at the intensity distribution? --Naomi At 06:01 AM 2/3/2009, Christian Bri?re wrote: >Hi! > >I am new in microarray analysis and in using Bioconductor. I need to >analyse expression data from monocolor Agilent microarrays (105K). To >my surprise, for each array (controls as well as treated samples) the >distribution of intensity data is bi-modal. Furthermore, it seems that >more than 10% of the genes are differentially expressed between controls >and treated samples. Therefore, I wonder what is the best method to use >in such case for between arrays normalization. I was told that median or >quantile normalization was not adequate. Should Invariant Set or VSN >normalization be better, and what are the packages to use for that ? >Thanks for your help > >-- > >Christian Bri?re >UMR CNRS-UPS 5546 >BP42617 Auzeville >F-31326 Castanet-Tolosan (France) >tel: +33(0)5 62 19 35 90 >Fax: +33(0)5 62 19 35 02 >E-mail: briere at scsv.ups-tlse.fr <mailto:briere at="" scsv.ups-="" tlse.fr=""> > >http://www.scsv.ups-tlse.fr >http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> >http://www.ifr40.cnrs.fr > > > > > [[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 Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
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On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi@stat.psu.edu> wrote: > Did you remove the Agilent controls before looking at the intensity > distribution? > > And are these some odd array design like tiling arrays? > > At 06:01 AM 2/3/2009, Christian Brière wrote: > >> Hi! >> >> I am new in microarray analysis and in using Bioconductor. I need to >> analyse expression data from monocolor Agilent microarrays (105K). To >> my surprise, for each array (controls as well as treated samples) the >> distribution of intensity data is bi-modal. Furthermore, it seems that >> more than 10% of the genes are differentially expressed between controls >> and treated samples. Therefore, I wonder what is the best method to use >> in such case for between arrays normalization. I was told that median or >> quantile normalization was not adequate. Should Invariant Set or VSN >> normalization be better, and what are the packages to use for that ? >> Thanks for your help >> >> -- >> >> Christian Brière >> UMR CNRS-UPS 5546 >> BP42617 Auzeville >> F-31326 Castanet-Tolosan (France) >> tel: +33(0)5 62 19 35 90 >> Fax: +33(0)5 62 19 35 02 >> E-mail: briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-tlse.fr> >> >> http://www.scsv.ups-tlse.fr >> http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> >> http://www.ifr40.cnrs.fr >> >> >> >> >> [[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 >> > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > _______________________________________________ > 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 > [[alternative HTML version deleted]]
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Yes, I removed Agilent controls and I filtered the data using Agilent flags (IsPosandSignificant and IsWellAboveBackground) before calculating intensity distribution. The array was designed using Tobacco contigs defined from available tobacco ESTs. My question is: whatever the origin of this bi-modal distribution, is it a problem for normalization and what kind of normalization is the most adequate ? Christian Sean Davis a écrit : > > > On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi@stat.psu.edu> <mailto:naomi@stat.psu.edu>> wrote: > > Did you remove the Agilent controls before looking at the > intensity distribution? > > > And are these some odd array design like tiling arrays? > > > > At 06:01 AM 2/3/2009, Christian Brière wrote: > > Hi! > > I am new in microarray analysis and in using Bioconductor. I > need to > analyse expression data from monocolor Agilent microarrays > (105K). To > my surprise, for each array (controls as well as treated > samples) the > distribution of intensity data is bi-modal. Furthermore, it > seems that > more than 10% of the genes are differentially expressed > between controls > and treated samples. Therefore, I wonder what is the best > method to use > in such case for between arrays normalization. I was told that > median or > quantile normalization was not adequate. Should Invariant Set > or VSN > normalization be better, and what are the packages to use for > that ? > Thanks for your help > > -- > > Christian Brière > UMR CNRS-UPS 5546 > BP42617 Auzeville > F-31326 Castanet-Tolosan (France) > tel: +33(0)5 62 19 35 90 > Fax: +33(0)5 62 19 35 02 > E-mail: briere@scsv.ups-tlse.fr > <mailto:briere@scsv.ups-tlse.fr> > <mailto:briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-="" tlse.fr="">> > > http://www.scsv.ups-tlse.fr > http://www.gdr2688.ups-tlse.fr > <http: www.gdr2688.ups-tlse.fr="" index.php=""> > http://www.ifr40.cnrs.fr > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > <mailto:bioconductor@stat.math.ethz.ch> > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 > (Statistics) > University Park, PA 16802-2111 > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch <mailto:bioconductor@stat.math.ethz.ch> > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > -- Christian Brière UMR CNRS-UPS 5546 BP42617 Auzeville F-31326 Castanet-Tolosan (France) tel: +33(0)5 62 19 35 90 Fax: +33(0)5 62 19 35 02 E-mail: briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-tlse.fr> http://www.scsv.ups-tlse.fr http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> http://www.ifr40.cnrs.fr [[alternative HTML version deleted]]
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Dear Christian the common normalisation methods assume that the normalisation between arrays involve one common transformation (linear in the case of median scaling, affine linear in the case of vsn with default parameter 'calib', local polynomial in the case of loess, non-parametric rank based in the case of quantile normalisation). I listed these in some order of flexibility. However, if you have two distinct populations of intensities, I would recommend first finding out the origin for those - try to look for associations of these two populations with all sorts of feature parameters (annotation, spatial position on the array) and make sure there is no show-stopper quality problem. Once you have that, the proper normalisation (perhaps stratified) will follow from that. Just applying one single overall transformation might be OK, but it could lead to distortions and inefficiency. Best wishes Wolfgang Christian Bri?re wrote: > Yes, I removed Agilent controls and I filtered the data using Agilent > flags (IsPosandSignificant and IsWellAboveBackground) before calculating > intensity distribution. > The array was designed using Tobacco contigs defined from available > tobacco ESTs. > My question is: whatever the origin of this bi-modal distribution, is it > a problem for normalization and what kind of normalization is the most > adequate ? > > Christian > > Sean Davis a ??crit : >> >> On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi at="" stat.psu.edu="">> <mailto:naomi at="" stat.psu.edu="">> wrote: >> >> Did you remove the Agilent controls before looking at the >> intensity distribution? >> >> >> And are these some odd array design like tiling arrays? >> >> >> >> At 06:01 AM 2/3/2009, Christian Bri??re wrote: >> >> Hi! >> >> I am new in microarray analysis and in using Bioconductor. I >> need to >> analyse expression data from monocolor Agilent microarrays >> (105K). To >> my surprise, for each array (controls as well as treated >> samples) the >> distribution of intensity data is bi-modal. Furthermore, it >> seems that >> more than 10% of the genes are differentially expressed >> between controls >> and treated samples. Therefore, I wonder what is the best >> method to use >> in such case for between arrays normalization. I was told that >> median or >> quantile normalization was not adequate. Should Invariant Set >> or VSN >> normalization be better, and what are the packages to use for >> that ? >> Thanks for your help >> >> -- >> >> Christian Bri??re >> UMR CNRS-UPS 5546 >> BP42617 Auzeville >> F-31326 Castanet-Tolosan (France) >> tel: +33(0)5 62 19 35 90 >> Fax: +33(0)5 62 19 35 02 >> E-mail: briere at scsv.ups-tlse.fr >> <mailto:briere at="" scsv.ups-tlse.fr=""> >> <mailto:briere at="" scsv.ups-tlse.fr="" <mailto:briere="" at="" scsv="" .ups-tlse.fr="">> >> >> http://www.scsv.ups-tlse.fr >> http://www.gdr2688.ups-tlse.fr >> <http: www.gdr2688.ups-tlse.fr="" index.php=""> >> http://www.ifr40.cnrs.fr >> >> >>
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Dear Wolfgang, Thank you for your advices. I did'nt find any spatial heterogeneity on the array which could explain a bi-modal distribution of intensity. One possibility is that we have two populations of probes corresponding to the two genomes of tobacco. But it is difficult to check. I tried different methods of global normalization, which seem to give approximately the same results. So, I wonder what kind of criteria I could use to select the "better" method ? Christian Wolfgang Huber a écrit : > Dear Christian > > the common normalisation methods assume that the normalisation between > arrays involve one common transformation (linear in the case of median > scaling, affine linear in the case of vsn with default parameter > 'calib', local polynomial in the case of loess, non-parametric rank > based in the case of quantile normalisation). I listed these in some > order of flexibility. > > However, if you have two distinct populations of intensities, I would > recommend first finding out the origin for those - try to look for > associations of these two populations with all sorts of feature > parameters (annotation, spatial position on the array) and make sure > there is no show-stopper quality problem. Once you have that, the > proper normalisation (perhaps stratified) will follow from that. > > Just applying one single overall transformation might be OK, but it > could lead to distortions and inefficiency. > > Best wishes > Wolfgang > > > > > Christian Brière wrote: >> Yes, I removed Agilent controls and I filtered the data using Agilent >> flags (IsPosandSignificant and IsWellAboveBackground) before >> calculating intensity distribution. >> The array was designed using Tobacco contigs defined from available >> tobacco ESTs. >> My question is: whatever the origin of this bi-modal distribution, is >> it a problem for normalization and what kind of normalization is the >> most adequate ? >> >> Christian >> >> Sean Davis a écrit : >>> >>> On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi@stat.psu.edu>>> <mailto:naomi@stat.psu.edu>> wrote: >>> >>> Did you remove the Agilent controls before looking at the >>> intensity distribution? >>> >>> >>> And are these some odd array design like tiling arrays? >>> >>> >>> >>> At 06:01 AM 2/3/2009, Christian Brière wrote: >>> >>> Hi! >>> >>> I am new in microarray analysis and in using Bioconductor. I >>> need to >>> analyse expression data from monocolor Agilent microarrays >>> (105K). To >>> my surprise, for each array (controls as well as treated >>> samples) the >>> distribution of intensity data is bi-modal. Furthermore, it >>> seems that >>> more than 10% of the genes are differentially expressed >>> between controls >>> and treated samples. Therefore, I wonder what is the best >>> method to use >>> in such case for between arrays normalization. I was told that >>> median or >>> quantile normalization was not adequate. Should Invariant Set >>> or VSN >>> normalization be better, and what are the packages to use for >>> that ? >>> Thanks for your help >>> >>> -- >>> >>> Christian Brière >>> UMR CNRS-UPS 5546 >>> BP42617 Auzeville >>> F-31326 Castanet-Tolosan (France) >>> tel: +33(0)5 62 19 35 90 >>> Fax: +33(0)5 62 19 35 02 >>> E-mail: briere@scsv.ups-tlse.fr >>> <mailto:briere@scsv.ups-tlse.fr> >>> <mailto:briere@scsv.ups-tlse.fr>>> <mailto:briere@scsv.ups-tlse.fr>> >>> >>> http://www.scsv.ups-tlse.fr >>> http://www.gdr2688.ups-tlse.fr >>> <http: www.gdr2688.ups-tlse.fr="" index.php=""> >>> http://www.ifr40.cnrs.fr >>> >>> >>> > -- Christian Brière UMR CNRS-UPS 5546 BP42617 Auzeville F-31326 Castanet-Tolosan (France) tel: +33(0)5 62 19 35 90 Fax: +33(0)5 62 19 35 02 E-mail: briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-tlse.fr> http://www.scsv.ups-tlse.fr http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> http://www.ifr40.cnrs.fr [[alternative HTML version deleted]]
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Dear Christian, well, if they give approximately the same results, then it doesn't really matter, does it? And if and where they disagree (i.e. if you do find substantially different sets of differentially expressed genes), look at the difference and decide whether it looks like extra sensitivity or an artifact to you. Sorry to be so vague, but if the answer were simpler, there would be no need for all these different normalisation methods. Thanks and best wishes Wolfgang > Dear Wolfgang, > > Thank you for your advices. I did'nt find any spatial heterogeneity on > the array which could explain a bi-modal distribution of intensity. One > possibility is that we have two populations of probes corresponding to > the two genomes of tobacco. But it is difficult to check. > I tried different methods of global normalization, which seem to give > approximately the same results. So, I wonder what kind of criteria I > could use to select the "better" method ? > > Christian > > > Wolfgang Huber a ?crit : >> Dear Christian >> >> the common normalisation methods assume that the normalisation between >> arrays involve one common transformation (linear in the case of median >> scaling, affine linear in the case of vsn with default parameter >> 'calib', local polynomial in the case of loess, non-parametric rank >> based in the case of quantile normalisation). I listed these in some >> order of flexibility. >> >> However, if you have two distinct populations of intensities, I would >> recommend first finding out the origin for those - try to look for >> associations of these two populations with all sorts of feature >> parameters (annotation, spatial position on the array) and make sure >> there is no show-stopper quality problem. Once you have that, the >> proper normalisation (perhaps stratified) will follow from that. >> >> Just applying one single overall transformation might be OK, but it >> could lead to distortions and inefficiency. >> >> Best wishes >> Wolfgang >> >> >> >> >> Christian Bri?re wrote: >>> Yes, I removed Agilent controls and I filtered the data using Agilent >>> flags (IsPosandSignificant and IsWellAboveBackground) before >>> calculating intensity distribution. >>> The array was designed using Tobacco contigs defined from available >>> tobacco ESTs. >>> My question is: whatever the origin of this bi-modal distribution, is >>> it a problem for normalization and what kind of normalization is the >>> most adequate ? >>> >>> Christian >>> >>> Sean Davis a ??crit : >>>> >>>> On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi at="" stat.psu.edu="">>>> <mailto:naomi at="" stat.psu.edu="">> wrote: >>>> >>>> Did you remove the Agilent controls before looking at the >>>> intensity distribution? >>>> >>>> >>>> And are these some odd array design like tiling arrays? >>>> >>>> >>>> >>>> At 06:01 AM 2/3/2009, Christian Bri??re wrote: >>>> >>>> Hi! >>>> >>>> I am new in microarray analysis and in using Bioconductor. I >>>> need to >>>> analyse expression data from monocolor Agilent microarrays >>>> (105K). To >>>> my surprise, for each array (controls as well as treated >>>> samples) the >>>> distribution of intensity data is bi-modal. Furthermore, it >>>> seems that >>>> more than 10% of the genes are differentially expressed >>>> between controls >>>> and treated samples. Therefore, I wonder what is the best >>>> method to use >>>> in such case for between arrays normalization. I was told that >>>> median or >>>> quantile normalization was not adequate. Should Invariant Set >>>> or VSN >>>> normalization be better, and what are the packages to use for >>>> that ? >>>> Thanks for your help >>>> >>>> -- >>>> >>>> Christian Bri??re >>>> UMR CNRS-UPS 5546 >>>> BP42617 Auzeville >>>> F-31326 Castanet-Tolosan (France) >>>> tel: +33(0)5 62 19 35 90 >>>> Fax: +33(0)5 62 19 35 02 >>>> E-mail: briere at scsv.ups-tlse.fr >>>> <mailto:briere at="" scsv.ups-tlse.fr=""> >>>> <mailto:briere at="" scsv.ups-tlse.fr="">>>> <mailto:briere at="" scsv.ups-tlse.fr="">> >>>> >>>> http://www.scsv.ups-tlse.fr >>>> http://www.gdr2688.ups-tlse.fr >>>> <http: www.gdr2688.ups-tlse.fr="" index.php=""> >>>> http://www.ifr40.cnrs.fr >>>> >>>> >>>> >> > > > -- > > Christian Bri?re > UMR CNRS-UPS 5546 > BP42617 Auzeville > F-31326 Castanet-Tolosan (France) > tel: +33(0)5 62 19 35 90 > Fax: +33(0)5 62 19 35 02 > E-mail: briere at scsv.ups-tlse.fr <mailto:briere at="" scsv.ups-="" tlse.fr=""> > > http://www.scsv.ups-tlse.fr > http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> > http://www.ifr40.cnrs.fr > > >
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Dear allWe have developed a method to normalize such bimodal expression data called RMA-MS (RMA multispecies). And yes, we do find that the number of genes found to be significant increases with RMA-MS vs traditional RMA, with much smaller q-values for the RMA-MS-normalized data. Dear Christian If you are interested we will be happy to share this procedure with you. Let me know. Stevens, John R., Balasubramanian Ganesan, Prerak Desai, Sweta Rao, and Bart C. Weimer. 2008. Statistical issues for normalization of multi-species microarray data. Proceedings of the Nineteenth Annual Kansas State University Conference on Applied Statistics in Agriculture. BALA. On Tue, Feb 10, 2009 at 1:20 PM, Wolfgang Huber <huber@ebi.ac.uk> wrote: > > Dear Christian, > > well, if they give approximately the same results, then it doesn't really > matter, does it? > > And if and where they disagree (i.e. if you do find substantially different > sets of differentially expressed genes), look at the difference and decide > whether it looks like extra sensitivity or an artifact to you. Sorry to be > so vague, but if the answer were simpler, there would be no need for all > these different normalisation methods. > > Thanks and best wishes > Wolfgang > > > > Dear Wolfgang, >> >> Thank you for your advices. I did'nt find any spatial heterogeneity on the >> array which could explain a bi-modal distribution of intensity. One >> possibility is that we have two populations of probes corresponding to the >> two genomes of tobacco. But it is difficult to check. >> I tried different methods of global normalization, which seem to give >> approximately the same results. So, I wonder what kind of criteria I could >> use to select the "better" method ? >> >> Christian >> >> >> Wolfgang Huber a écrit : >> >>> Dear Christian >>> >>> the common normalisation methods assume that the normalisation between >>> arrays involve one common transformation (linear in the case of median >>> scaling, affine linear in the case of vsn with default parameter 'calib', >>> local polynomial in the case of loess, non-parametric rank based in the case >>> of quantile normalisation). I listed these in some order of flexibility. >>> >>> However, if you have two distinct populations of intensities, I would >>> recommend first finding out the origin for those - try to look for >>> associations of these two populations with all sorts of feature parameters >>> (annotation, spatial position on the array) and make sure there is no >>> show-stopper quality problem. Once you have that, the proper normalisation >>> (perhaps stratified) will follow from that. >>> >>> Just applying one single overall transformation might be OK, but it could >>> lead to distortions and inefficiency. >>> >>> Best wishes >>> Wolfgang >>> >>> >>> >>> >>> Christian Brière wrote: >>> >>>> Yes, I removed Agilent controls and I filtered the data using Agilent >>>> flags (IsPosandSignificant and IsWellAboveBackground) before calculating >>>> intensity distribution. >>>> The array was designed using Tobacco contigs defined from available >>>> tobacco ESTs. >>>> My question is: whatever the origin of this bi-modal distribution, is it >>>> a problem for normalization and what kind of normalization is the most >>>> adequate ? >>>> >>>> Christian >>>> >>>> Sean Davis a Ã(c)crit : >>>> >>>>> >>>>> On Tue, Feb 3, 2009 at 8:03 AM, Naomi Altman <naomi@stat.psu.edu<mailto:>>>>> naomi@stat.psu.edu>> wrote: >>>>> >>>>> Did you remove the Agilent controls before looking at the >>>>> intensity distribution? >>>>> >>>>> >>>>> And are these some odd array design like tiling arrays? >>>>> >>>>> >>>>> At 06:01 AM 2/3/2009, Christian Brière wrote: >>>>> >>>>> Hi! >>>>> >>>>> I am new in microarray analysis and in using Bioconductor. I >>>>> need to >>>>> analyse expression data from monocolor Agilent microarrays >>>>> (105K). To >>>>> my surprise, for each array (controls as well as treated >>>>> samples) the >>>>> distribution of intensity data is bi-modal. Furthermore, it >>>>> seems that >>>>> more than 10% of the genes are differentially expressed >>>>> between controls >>>>> and treated samples. Therefore, I wonder what is the best >>>>> method to use >>>>> in such case for between arrays normalization. I was told that >>>>> median or >>>>> quantile normalization was not adequate. Should Invariant Set >>>>> or VSN >>>>> normalization be better, and what are the packages to use for >>>>> that ? >>>>> Thanks for your help >>>>> >>>>> -- >>>>> >>>>> Christian Brière >>>>> UMR CNRS-UPS 5546 >>>>> BP42617 Auzeville >>>>> F-31326 Castanet-Tolosan (France) >>>>> tel: +33(0)5 62 19 35 90 >>>>> Fax: +33(0)5 62 19 35 02 >>>>> E-mail: briere@scsv.ups-tlse.fr >>>>> <mailto:briere@scsv.ups-tlse.fr> >>>>> <mailto:briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-="" tlse.fr="">>>>> >> >>>>> >>>>> http://www.scsv.ups-tlse.fr >>>>> http://www.gdr2688.ups-tlse.fr >>>>> <http: www.gdr2688.ups-tlse.fr="" index.php=""> >>>>> http://www.ifr40.cnrs.fr >>>>> >>>>> >>>>> >>>>> >>> >> >> -- >> >> Christian Brière >> UMR CNRS-UPS 5546 >> BP42617 Auzeville >> F-31326 Castanet-Tolosan (France) >> tel: +33(0)5 62 19 35 90 >> Fax: +33(0)5 62 19 35 02 >> E-mail: briere@scsv.ups-tlse.fr <mailto:briere@scsv.ups-tlse.fr> >> >> http://www.scsv.ups-tlse.fr >> http://www.gdr2688.ups-tlse.fr <http: www.gdr2688.ups-="" tlse.fr="" index.php=""> >> http://www.ifr40.cnrs.fr >> >> >> > > _______________________________________________ > 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 > [[alternative HTML version deleted]]
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2009/2/11 Balasubramanian Ganesan <bala.g at="" aggiemail.usu.edu="">: > Dear allWe have developed a method to normalize such bimodal expression data > called RMA-MS (RMA multispecies). > And yes, we do find that the number of genes found to be significant > increases with RMA-MS vs traditional RMA, with much smaller q-values for the > RMA-MS-normalized data. > Dear Christian > If you are interested we will be happy to share this procedure with you. > Let me know. > > Stevens, John R., Balasubramanian Ganesan, Prerak Desai, Sweta Rao, and Bart > C. Weimer. 2008. Statistical issues for normalization of multi- species > microarray data. Proceedings of the Nineteenth Annual Kansas State > University Conference on Applied Statistics in Agriculture. > > BALA. Dear Ganesan, I'm very interested in your bi-modal normalization method. I would like to apply it on one color nylon membrane array data. Please, could you share it with me? The paper and any how to is very welcome. Thank you very much Marcelo -- Marcelo Luiz de Laia Jaboticabal - SP - Brazil Please avoid sending me Word or PowerPoint attachments. See: http://www.gnu.org/philosophy/no-word-attachments.html http://www.gnu.org/philosophy/no-word-attachments.pt-br.html "Qual ? a minha expectativa, e por que eu sou petista, e por que com todos os desastres deste partido, eu continuo nele? Porque acho que temos um processo hist?rico lento a realizar, que come?ou muito antes de mim, e que os meus bisnetos v?o finalizar." Marilena Chaui
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