Detection calls and LIMMA
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@gordon-smyth
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WEHI, Melbourne, Australia
Deaer Avhena, I agree with Wolfgang that filtering is useful. In my lab, the standard practice is to filter probes that fail to show some modest evidence for expression on at least n arrays, where n is the minimum group size. For example, if we compare wt (with 2 replicate arrays) to a mutant (with 3 replicate arrays), we filter probes that are Present on fewer than 2 arrays. This is because we want to keep any probe that is expressed in at least one of the experimental conditions. If a probe is expressed in one of the conditions, then it should appear consistently across the replicates for that condition. Best wishes Gordon > Date: Mon, 28 Feb 2011 11:35:42 -0500 > From: avehna <avhena at="" gmail.com=""> > To: whuber at embl.de > Cc: bioconductor at r-project.org > Subject: Re: [BioC] Detection calls and LIMMA > > Dear Wolfgang, > > Thank you for your response, I agree with you. I will read the paper now... > > Best Regards, > Avhena > > On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> wrote: > >> Hi Avhena >> >> it is not required, but properly applied filtering can increase detection >> power in your experiment while still controlling type-I error (false >> positives). The example you mention seems to be one that you want to keep >> though, since it is a good candidate for being up-regulated in the Treatment >> condition. One possibly reasonable criterion would be, e.g., to filter out >> all probesets that are called 'Absent' on all arrays. Some further >> discussion on the topic is also here: >> >> [1] Bourgon, Gentleman and Huber. Independent filtering increases detection >> power for high-throughput experiments. PNAS, 107(21):9546-9551, >> >> Best wishes >> Wolfgang >> >> >> Il Feb/28/11 6:51 AM, avehna ha scritto: >> >>> Hi All, >>> >>> I have a basic question. Is it required to filter the microarray data >>> based >>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>> >>> What if I have the following scenario (for example): >>> >>> Control Control Control Treatment >>> Treatment Treatment >>> 1367813_at A A P >>> P P P >>> >>> Please note that this gene is just "present/detected" once in the >>> Control, >>> but it is present in all the replicates of the treatment. In this case: >>> what >>> would be the right thing to do? To eliminate it from the analysis or keep >>> it >>> and consider it up or down depending on the signal of the treatment? >>> >>> Thank a lot! >>> Avhena ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
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@richard-friedman-513
Last seen 9.6 years ago
Dear Gordon, Can you suggest how to define "some modest evidence of expression" in Affymetrix arrays filtered with RMA or GCRMA which does not give a presence-or-absence call? Thanks and best wishes, Rich On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: > Deaer Avhena, > > I agree with Wolfgang that filtering is useful. In my lab, the > standard practice is to filter probes that fail to show some modest > evidence for expression on at least n arrays, where n is the minimum > group size. For example, if we compare wt (with 2 replicate arrays) > to a mutant (with 3 replicate arrays), we filter probes that are > Present on fewer than 2 arrays. > > This is because we want to keep any probe that is expressed in at > least one of the experimental conditions. If a probe is expressed > in one of the conditions, then it should appear consistently across > the replicates for that condition. > > Best wishes > Gordon > >> Date: Mon, 28 Feb 2011 11:35:42 -0500 >> From: avehna <avhena at="" gmail.com=""> >> To: whuber at embl.de >> Cc: bioconductor at r-project.org >> Subject: Re: [BioC] Detection calls and LIMMA >> >> Dear Wolfgang, >> >> Thank you for your response, I agree with you. I will read the >> paper now... >> >> Best Regards, >> Avhena >> >> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >> wrote: >> >>> Hi Avhena >>> >>> it is not required, but properly applied filtering can increase >>> detection >>> power in your experiment while still controlling type-I error (false >>> positives). The example you mention seems to be one that you want >>> to keep >>> though, since it is a good candidate for being up-regulated in the >>> Treatment >>> condition. One possibly reasonable criterion would be, e.g., to >>> filter out >>> all probesets that are called 'Absent' on all arrays. Some further >>> discussion on the topic is also here: >>> >>> [1] Bourgon, Gentleman and Huber. Independent filtering increases >>> detection >>> power for high-throughput experiments. PNAS, 107(21):9546-9551, >>> >>> Best wishes >>> Wolfgang >>> >>> >>> Il Feb/28/11 6:51 AM, avehna ha scritto: >>> >>>> Hi All, >>>> >>>> I have a basic question. Is it required to filter the microarray >>>> data >>>> based >>>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>>> >>>> What if I have the following scenario (for example): >>>> >>>> Control Control Control Treatment >>>> Treatment Treatment >>>> 1367813_at A A P >>>> P P P >>>> >>>> Please note that this gene is just "present/detected" once in the >>>> Control, >>>> but it is present in all the replicates of the treatment. In this >>>> case: >>>> what >>>> would be the right thing to do? To eliminate it from the analysis >>>> or keep >>>> it >>>> and consider it up or down depending on the signal of the >>>> treatment? >>>> >>>> Thank a lot! >>>> Avhena > > ______________________________________________________________________ > The information in this email is confidential and intend...{{dropped: > 4}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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Jenny Drnevich ★ 2.0k
@jenny-drnevich-2812
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Hi Rich, You can use the P/M/A calls to filter regardless of what pre-processing algorithm you use. It is a completely separate algorithm from MAS5, and I routinely use the P/M/A calls from the mas5calls() function in the affy package in conjunction with gcrma values. Cheers, Jenny At 01:14 PM 3/2/2011, Richard Friedman wrote: >Dear Gordon, > > Can you suggest how to define "some modest evidence of expression" >in Affymetrix arrays filtered with RMA >or GCRMA which does not give a presence-or-absence call? > >Thanks and best wishes, >Rich > >On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: > >>Deaer Avhena, >> >>I agree with Wolfgang that filtering is useful. In my lab, the >>standard practice is to filter probes that fail to show some modest >>evidence for expression on at least n arrays, where n is the minimum >>group size. For example, if we compare wt (with 2 replicate arrays) >>to a mutant (with 3 replicate arrays), we filter probes that are >>Present on fewer than 2 arrays. >> >>This is because we want to keep any probe that is expressed in at >>least one of the experimental conditions. If a probe is expressed >>in one of the conditions, then it should appear consistently across >>the replicates for that condition. >> >>Best wishes >>Gordon >> >>>Date: Mon, 28 Feb 2011 11:35:42 -0500 >>>From: avehna <avhena at="" gmail.com=""> >>>To: whuber at embl.de >>>Cc: bioconductor at r-project.org >>>Subject: Re: [BioC] Detection calls and LIMMA >>> >>>Dear Wolfgang, >>> >>>Thank you for your response, I agree with you. I will read the >>>paper now... >>> >>>Best Regards, >>>Avhena >>> >>>On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >>>wrote: >>> >>>>Hi Avhena >>>> >>>>it is not required, but properly applied filtering can increase >>>>detection >>>>power in your experiment while still controlling type-I error (false >>>>positives). The example you mention seems to be one that you want >>>>to keep >>>>though, since it is a good candidate for being up-regulated in the >>>>Treatment >>>>condition. One possibly reasonable criterion would be, e.g., to >>>>filter out >>>>all probesets that are called 'Absent' on all arrays. Some further >>>>discussion on the topic is also here: >>>> >>>>[1] Bourgon, Gentleman and Huber. Independent filtering increases >>>>detection >>>>power for high-throughput experiments. PNAS, 107(21):9546-9551, >>>> >>>> Best wishes >>>> Wolfgang >>>> >>>> >>>>Il Feb/28/11 6:51 AM, avehna ha scritto: >>>> >>>>>Hi All, >>>>> >>>>>I have a basic question. Is it required to filter the microarray >>>>>data >>>>>based >>>>>on the detection calls (A/M/P) before analyzing it with LIMMA? >>>>> >>>>>What if I have the following scenario (for example): >>>>> >>>>> Control Control Control Treatment >>>>>Treatment Treatment >>>>>1367813_at A A P >>>>>P P P >>>>> >>>>>Please note that this gene is just "present/detected" once in the >>>>>Control, >>>>>but it is present in all the replicates of the treatment. In this >>>>>case: >>>>>what >>>>>would be the right thing to do? To eliminate it from the analysis >>>>>or keep >>>>>it >>>>>and consider it up or down depending on the signal of the >>>>>treatment? >>>>> >>>>>Thank a lot! >>>>>Avhena >> >>____________________________________________________________________ __ >>The information in this email is confidential and intend...{{dropped: 4}} >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at r-project.org >>https://stat.ethz.ch/mailman/listinfo/bioconductor >>Search the archives: >>http://news.gmane.org/gmane.science.biology.informatics.conductor > >_______________________________________________ >Bioconductor mailing list >Bioconductor at r-project.org >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor
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Jenny and list, Am I correct that P/M/A calls depend upon the presence of mismatched genes? If so, is there a way to filter RMA normalized ST1.0 genes by present absent? Thanks and best wishes, Rich On Mar 2, 2011, at 4:04 PM, Jenny Drnevich wrote: > Hi Rich, > > You can use the P/M/A calls to filter regardless of what pre- > processing algorithm you use. It is a completely separate algorithm > from MAS5, and I routinely use the P/M/A calls from the mas5calls() > function in the affy package in conjunction with gcrma values. > > Cheers, > Jenny > > At 01:14 PM 3/2/2011, Richard Friedman wrote: >> Dear Gordon, >> >> Can you suggest how to define "some modest evidence of >> expression" >> in Affymetrix arrays filtered with RMA >> or GCRMA which does not give a presence-or-absence call? >> >> Thanks and best wishes, >> Rich >> >> On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: >> >>> Deaer Avhena, >>> >>> I agree with Wolfgang that filtering is useful. In my lab, the >>> standard practice is to filter probes that fail to show some modest >>> evidence for expression on at least n arrays, where n is the minimum >>> group size. For example, if we compare wt (with 2 replicate arrays) >>> to a mutant (with 3 replicate arrays), we filter probes that are >>> Present on fewer than 2 arrays. >>> >>> This is because we want to keep any probe that is expressed in at >>> least one of the experimental conditions. If a probe is expressed >>> in one of the conditions, then it should appear consistently across >>> the replicates for that condition. >>> >>> Best wishes >>> Gordon >>> >>>> Date: Mon, 28 Feb 2011 11:35:42 -0500 >>>> From: avehna <avhena at="" gmail.com=""> >>>> To: whuber at embl.de >>>> Cc: bioconductor at r-project.org >>>> Subject: Re: [BioC] Detection calls and LIMMA >>>> >>>> Dear Wolfgang, >>>> >>>> Thank you for your response, I agree with you. I will read the >>>> paper now... >>>> >>>> Best Regards, >>>> Avhena >>>> >>>> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >>>> wrote: >>>> >>>>> Hi Avhena >>>>> >>>>> it is not required, but properly applied filtering can increase >>>>> detection >>>>> power in your experiment while still controlling type-I error >>>>> (false >>>>> positives). The example you mention seems to be one that you want >>>>> to keep >>>>> though, since it is a good candidate for being up-regulated in the >>>>> Treatment >>>>> condition. One possibly reasonable criterion would be, e.g., to >>>>> filter out >>>>> all probesets that are called 'Absent' on all arrays. Some further >>>>> discussion on the topic is also here: >>>>> >>>>> [1] Bourgon, Gentleman and Huber. Independent filtering increases >>>>> detection >>>>> power for high-throughput experiments. PNAS, 107(21):9546-9551, >>>>> >>>>> Best wishes >>>>> Wolfgang >>>>> >>>>> >>>>> Il Feb/28/11 6:51 AM, avehna ha scritto: >>>>> >>>>>> Hi All, >>>>>> >>>>>> I have a basic question. Is it required to filter the microarray >>>>>> data >>>>>> based >>>>>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>>>>> >>>>>> What if I have the following scenario (for example): >>>>>> >>>>>> Control Control Control >>>>>> Treatment >>>>>> Treatment Treatment >>>>>> 1367813_at A A P >>>>>> P P P >>>>>> >>>>>> Please note that this gene is just "present/detected" once in >>>>>> the >>>>>> Control, >>>>>> but it is present in all the replicates of the treatment. In this >>>>>> case: >>>>>> what >>>>>> would be the right thing to do? To eliminate it from the analysis >>>>>> or keep >>>>>> it >>>>>> and consider it up or down depending on the signal of the >>>>>> treatment? >>>>>> >>>>>> Thank a lot! >>>>>> Avhena >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intend... >>> {{dropped: 4}} >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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Hi Rich, On 3/7/2011 1:30 PM, Richard Friedman wrote: > Jenny and list, > > Am I correct that P/M/A calls depend upon the presence of mismatched genes? > If so, is there a way to filter RMA normalized ST1.0 genes by present > absent? No, P/M/A are based on a Wilcoxon signed-rank test that compares the expression levels of the PM and MM probes. If you don't have matched PM and MM probes, you won't be able to do these calls. Best, Jim > > Thanks and best wishes, > Rich > > > On Mar 2, 2011, at 4:04 PM, Jenny Drnevich wrote: > >> Hi Rich, >> >> You can use the P/M/A calls to filter regardless of what >> pre-processing algorithm you use. It is a completely separate >> algorithm from MAS5, and I routinely use the P/M/A calls from the >> mas5calls() function in the affy package in conjunction with gcrma >> values. >> >> Cheers, >> Jenny >> >> At 01:14 PM 3/2/2011, Richard Friedman wrote: >>> Dear Gordon, >>> >>> Can you suggest how to define "some modest evidence of expression" >>> in Affymetrix arrays filtered with RMA >>> or GCRMA which does not give a presence-or-absence call? >>> >>> Thanks and best wishes, >>> Rich >>> >>> On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: >>> >>>> Deaer Avhena, >>>> >>>> I agree with Wolfgang that filtering is useful. In my lab, the >>>> standard practice is to filter probes that fail to show some modest >>>> evidence for expression on at least n arrays, where n is the minimum >>>> group size. For example, if we compare wt (with 2 replicate arrays) >>>> to a mutant (with 3 replicate arrays), we filter probes that are >>>> Present on fewer than 2 arrays. >>>> >>>> This is because we want to keep any probe that is expressed in at >>>> least one of the experimental conditions. If a probe is expressed >>>> in one of the conditions, then it should appear consistently across >>>> the replicates for that condition. >>>> >>>> Best wishes >>>> Gordon >>>> >>>>> Date: Mon, 28 Feb 2011 11:35:42 -0500 >>>>> From: avehna <avhena at="" gmail.com=""> >>>>> To: whuber at embl.de >>>>> Cc: bioconductor at r-project.org >>>>> Subject: Re: [BioC] Detection calls and LIMMA >>>>> >>>>> Dear Wolfgang, >>>>> >>>>> Thank you for your response, I agree with you. I will read the >>>>> paper now... >>>>> >>>>> Best Regards, >>>>> Avhena >>>>> >>>>> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >>>>> wrote: >>>>> >>>>>> Hi Avhena >>>>>> >>>>>> it is not required, but properly applied filtering can increase >>>>>> detection >>>>>> power in your experiment while still controlling type-I error (false >>>>>> positives). The example you mention seems to be one that you want >>>>>> to keep >>>>>> though, since it is a good candidate for being up-regulated in the >>>>>> Treatment >>>>>> condition. One possibly reasonable criterion would be, e.g., to >>>>>> filter out >>>>>> all probesets that are called 'Absent' on all arrays. Some further >>>>>> discussion on the topic is also here: >>>>>> >>>>>> [1] Bourgon, Gentleman and Huber. Independent filtering increases >>>>>> detection >>>>>> power for high-throughput experiments. PNAS, 107(21):9546-9551, >>>>>> >>>>>> Best wishes >>>>>> Wolfgang >>>>>> >>>>>> >>>>>> Il Feb/28/11 6:51 AM, avehna ha scritto: >>>>>> >>>>>>> Hi All, >>>>>>> >>>>>>> I have a basic question. Is it required to filter the microarray >>>>>>> data >>>>>>> based >>>>>>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>>>>>> >>>>>>> What if I have the following scenario (for example): >>>>>>> >>>>>>> Control Control Control Treatment >>>>>>> Treatment Treatment >>>>>>> 1367813_at A A P >>>>>>> P P P >>>>>>> >>>>>>> Please note that this gene is just "present/detected" once in the >>>>>>> Control, >>>>>>> but it is present in all the replicates of the treatment. In this >>>>>>> case: >>>>>>> what >>>>>>> would be the right thing to do? To eliminate it from the analysis >>>>>>> or keep >>>>>>> it >>>>>>> and consider it up or down depending on the signal of the >>>>>>> treatment? >>>>>>> >>>>>>> Thank a lot! >>>>>>> Avhena >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and >>>> intend...{{dropped: 4}} >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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Dear Jim, Sam, and Christian. I will look into all of your suggestions. Expect more questions. Best wishes, Rich On Mar 7, 2011, at 1:59 PM, James W. MacDonald wrote: > Hi Rich, > > On 3/7/2011 1:30 PM, Richard Friedman wrote: >> Jenny and list, >> >> Am I correct that P/M/A calls depend upon the presence of >> mismatched genes? >> If so, is there a way to filter RMA normalized ST1.0 genes by present >> absent? > > No, P/M/A are based on a Wilcoxon signed-rank test that compares the > expression levels of the PM and MM probes. If you don't have matched > PM and MM probes, you won't be able to do these calls. > > Best, > > Jim > > >> >> Thanks and best wishes, >> Rich >> >> >> On Mar 2, 2011, at 4:04 PM, Jenny Drnevich wrote: >> >>> Hi Rich, >>> >>> You can use the P/M/A calls to filter regardless of what >>> pre-processing algorithm you use. It is a completely separate >>> algorithm from MAS5, and I routinely use the P/M/A calls from the >>> mas5calls() function in the affy package in conjunction with gcrma >>> values. >>> >>> Cheers, >>> Jenny >>> >>> At 01:14 PM 3/2/2011, Richard Friedman wrote: >>>> Dear Gordon, >>>> >>>> Can you suggest how to define "some modest evidence of expression" >>>> in Affymetrix arrays filtered with RMA >>>> or GCRMA which does not give a presence-or-absence call? >>>> >>>> Thanks and best wishes, >>>> Rich >>>> >>>> On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: >>>> >>>>> Deaer Avhena, >>>>> >>>>> I agree with Wolfgang that filtering is useful. In my lab, the >>>>> standard practice is to filter probes that fail to show some >>>>> modest >>>>> evidence for expression on at least n arrays, where n is the >>>>> minimum >>>>> group size. For example, if we compare wt (with 2 replicate >>>>> arrays) >>>>> to a mutant (with 3 replicate arrays), we filter probes that are >>>>> Present on fewer than 2 arrays. >>>>> >>>>> This is because we want to keep any probe that is expressed in at >>>>> least one of the experimental conditions. If a probe is expressed >>>>> in one of the conditions, then it should appear consistently >>>>> across >>>>> the replicates for that condition. >>>>> >>>>> Best wishes >>>>> Gordon >>>>> >>>>>> Date: Mon, 28 Feb 2011 11:35:42 -0500 >>>>>> From: avehna <avhena at="" gmail.com=""> >>>>>> To: whuber at embl.de >>>>>> Cc: bioconductor at r-project.org >>>>>> Subject: Re: [BioC] Detection calls and LIMMA >>>>>> >>>>>> Dear Wolfgang, >>>>>> >>>>>> Thank you for your response, I agree with you. I will read the >>>>>> paper now... >>>>>> >>>>>> Best Regards, >>>>>> Avhena >>>>>> >>>>>> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >>>>>> wrote: >>>>>> >>>>>>> Hi Avhena >>>>>>> >>>>>>> it is not required, but properly applied filtering can increase >>>>>>> detection >>>>>>> power in your experiment while still controlling type-I error >>>>>>> (false >>>>>>> positives). The example you mention seems to be one that you >>>>>>> want >>>>>>> to keep >>>>>>> though, since it is a good candidate for being up-regulated in >>>>>>> the >>>>>>> Treatment >>>>>>> condition. One possibly reasonable criterion would be, e.g., to >>>>>>> filter out >>>>>>> all probesets that are called 'Absent' on all arrays. Some >>>>>>> further >>>>>>> discussion on the topic is also here: >>>>>>> >>>>>>> [1] Bourgon, Gentleman and Huber. Independent filtering >>>>>>> increases >>>>>>> detection >>>>>>> power for high-throughput experiments. PNAS, 107(21):9546-9551, >>>>>>> >>>>>>> Best wishes >>>>>>> Wolfgang >>>>>>> >>>>>>> >>>>>>> Il Feb/28/11 6:51 AM, avehna ha scritto: >>>>>>> >>>>>>>> Hi All, >>>>>>>> >>>>>>>> I have a basic question. Is it required to filter the >>>>>>>> microarray >>>>>>>> data >>>>>>>> based >>>>>>>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>>>>>>> >>>>>>>> What if I have the following scenario (for example): >>>>>>>> >>>>>>>> Control Control Control Treatment >>>>>>>> Treatment Treatment >>>>>>>> 1367813_at A A P >>>>>>>> P P P >>>>>>>> >>>>>>>> Please note that this gene is just "present/detected" once in >>>>>>>> the >>>>>>>> Control, >>>>>>>> but it is present in all the replicates of the treatment. In >>>>>>>> this >>>>>>>> case: >>>>>>>> what >>>>>>>> would be the right thing to do? To eliminate it from the >>>>>>>> analysis >>>>>>>> or keep >>>>>>>> it >>>>>>>> and consider it up or down depending on the signal of the >>>>>>>> treatment? >>>>>>>> >>>>>>>> Thank a lot! >>>>>>>> Avhena >>>>> >>>>> ______________________________________________________________________ >>>>> The information in this email is confidential and >>>>> intend...{{dropped: 4}} >>>>> >>>>> _______________________________________________ >>>>> Bioconductor mailing list >>>>> Bioconductor at r-project.org >>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>>> Search the archives: >>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > -- > James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should > not be used for urgent or sensitive issues
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Dear Rich, Please correct me if I am wrong, but the question is basically whether any sort of estimate P/A exists with PM-only arrays? I recall a discussion where alternative options were proposed, and as far as I know none of the alternatives is stream-lined for all array types. But if you want to invest a bit of effort, such a method could be adapted for your array type using ideas from different approaches: a) the PANP-method (available for the HGU133-series, implemented in the PANP package available in Bioc) developed by Peter Warren and colleagues (I have adapted a version for the ATH1-Arabidopsis chip myself, using probes that do not match newer genome releases anymore; this could easily be done by BLAST for example): www.people.brandeis.edu/~dtaylor/Taylor_Papers/panp.pdf b) the half-price method proposed by Wu and Irizarry (as far as I know the code is available upon request): http://www.bepress.com/jhubiostat/paper73 c) the developers of the Rosetta software have proposed a further method to calculate present/absent p-values based on a Gaussian distribution with parameters estimated from negative probes (Weng et al, Bioinformatics (2006) 22 (9): 1111-1121. http://bioinformatics.oxfordjournals.org/content/22/9/1111.full). I guess the method has been implemented in their software? But it might be adapted in R too... Maybe there are further suggestions? Hope this helps anyway... Best wishes, Sam On 7 March 2011 18:30, Richard Friedman <friedman at="" cancercenter.columbia.edu=""> wrote: > Jenny and list, > > ? ? ? ?Am I correct that P/M/A calls depend upon the presence of mismatched > genes? > If so, is there a way to filter RMA normalized ST1.0 genes by present > absent? > > Thanks and best wishes, > Rich > > > On Mar 2, 2011, at 4:04 PM, Jenny Drnevich wrote: > >> Hi Rich, >> >> You can use the P/M/A calls to filter regardless of what pre- processing >> algorithm you use. It is a completely separate algorithm from MAS5, and I >> routinely use the P/M/A calls from the mas5calls() function in the affy >> package in conjunction with gcrma values. >> >> Cheers, >> Jenny >> >> At 01:14 PM 3/2/2011, Richard Friedman wrote: >>> >>> Dear Gordon, >>> >>> ? ? ? Can you ?suggest how to define "some modest evidence of expression" >>> in Affymetrix arrays filtered with RMA >>> or GCRMA which does not give a presence-or-absence call? >>> >>> Thanks and best wishes, >>> Rich >>> >>> On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: >>> >>>> Deaer Avhena, >>>> >>>> I agree with Wolfgang that filtering is useful. ?In my lab, the >>>> standard practice is to filter probes that fail to show some modest >>>> evidence for expression on at least n arrays, where n is the minimum >>>> group size. ?For example, if we compare wt (with 2 replicate arrays) >>>> to a mutant (with 3 replicate arrays), we filter probes that are >>>> Present on fewer than 2 arrays. >>>> >>>> This is because we want to keep any probe that is expressed in at >>>> least one of the experimental conditions. ?If a probe is expressed >>>> in one of the conditions, then it should appear consistently across >>>> the replicates for that condition. >>>> >>>> Best wishes >>>> Gordon >>>> >>>>> Date: Mon, 28 Feb 2011 11:35:42 -0500 >>>>> From: avehna <avhena at="" gmail.com=""> >>>>> To: whuber at embl.de >>>>> Cc: bioconductor at r-project.org >>>>> Subject: Re: [BioC] Detection calls and LIMMA >>>>> >>>>> Dear Wolfgang, >>>>> >>>>> Thank you for your response, I agree with you. I will read the >>>>> paper now... >>>>> >>>>> Best Regards, >>>>> Avhena >>>>> >>>>> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >>>>> wrote: >>>>> >>>>>> Hi Avhena >>>>>> >>>>>> it is not required, but properly applied filtering can increase >>>>>> detection >>>>>> power in your experiment while still controlling type-I error (false >>>>>> positives). The example you mention seems to be one that you want >>>>>> to keep >>>>>> though, since it is a good candidate for being up-regulated in the >>>>>> Treatment >>>>>> condition. One possibly reasonable criterion would be, e.g., to >>>>>> filter out >>>>>> all probesets that are called 'Absent' on all arrays. Some further >>>>>> discussion on the topic is also here: >>>>>> >>>>>> [1] Bourgon, Gentleman and Huber. Independent filtering increases >>>>>> detection >>>>>> power for high-throughput experiments. PNAS, 107(21):9546-9551, >>>>>> >>>>>> ? ? Best wishes >>>>>> ? ? Wolfgang >>>>>> >>>>>> >>>>>> Il Feb/28/11 6:51 AM, avehna ha scritto: >>>>>> >>>>>>> Hi All, >>>>>>> >>>>>>> I have a basic question. Is it required to filter the microarray >>>>>>> data >>>>>>> based >>>>>>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>>>>>> >>>>>>> What if I have the following scenario (for example): >>>>>>> >>>>>>> ? ? ? ? ? ? ? ? ? ? ? ? Control Control Control ? ? ? ? Treatment >>>>>>> Treatment Treatment >>>>>>> 1367813_at ? ? ? ? ? A ? ? ? ? ? ?A ? ? ? ? ? ? P >>>>>>> P ? ? ? ? ? ? ? ? ? P ? ? ? ? ? ? ? ? P >>>>>>> >>>>>>> Please note that this gene is just "present/detected" ?once in the >>>>>>> Control, >>>>>>> but it is present in all the replicates of the treatment. In this >>>>>>> case: >>>>>>> what >>>>>>> would be the right thing to do? To eliminate it from the analysis >>>>>>> or keep >>>>>>> it >>>>>>> and consider it up or down depending on the signal of the >>>>>>> treatment? >>>>>>> >>>>>>> Thank a lot! >>>>>>> Avhena >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and intend...{{dropped: >>>> 4}} >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > ------------------------------------------------------- Samuel Wuest Smurfit Institute of Genetics Trinity College Dublin Dublin 2, Ireland Phone: +353-1-896 2444 Web: http://www.tcd.ie/Genetics/wellmer-2/index.html Email: wuests at tcd.ie
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Dear Richard, For Gene ST arrays Affymetrix has developed DABG as algorithm for P/M/A calls. Using DABG you can filter RMA-normalized genes. As far as I know DABG is currently only supported by APT and by xps. (In addition xps allows you in principle to use the original detection call algorithm for Gene ST arrays, too.) Best regards Christian _._._._._._._._._._._._._._._._._._ C.h.r.i.s.t.i.a.n S.t.r.a.t.o.w.a V.i.e.n.n.a A.u.s.t.r.i.a e.m.a.i.l: cstrato at aon.at _._._._._._._._._._._._._._._._._._ On 3/7/11 7:30 PM, Richard Friedman wrote: > Jenny and list, > > Am I correct that P/M/A calls depend upon the presence of mismatched genes? > If so, is there a way to filter RMA normalized ST1.0 genes by present > absent? > > Thanks and best wishes, > Rich > > > On Mar 2, 2011, at 4:04 PM, Jenny Drnevich wrote: > >> Hi Rich, >> >> You can use the P/M/A calls to filter regardless of what >> pre-processing algorithm you use. It is a completely separate >> algorithm from MAS5, and I routinely use the P/M/A calls from the >> mas5calls() function in the affy package in conjunction with gcrma >> values. >> >> Cheers, >> Jenny >> >> At 01:14 PM 3/2/2011, Richard Friedman wrote: >>> Dear Gordon, >>> >>> Can you suggest how to define "some modest evidence of expression" >>> in Affymetrix arrays filtered with RMA >>> or GCRMA which does not give a presence-or-absence call? >>> >>> Thanks and best wishes, >>> Rich >>> >>> On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote: >>> >>>> Deaer Avhena, >>>> >>>> I agree with Wolfgang that filtering is useful. In my lab, the >>>> standard practice is to filter probes that fail to show some modest >>>> evidence for expression on at least n arrays, where n is the minimum >>>> group size. For example, if we compare wt (with 2 replicate arrays) >>>> to a mutant (with 3 replicate arrays), we filter probes that are >>>> Present on fewer than 2 arrays. >>>> >>>> This is because we want to keep any probe that is expressed in at >>>> least one of the experimental conditions. If a probe is expressed >>>> in one of the conditions, then it should appear consistently across >>>> the replicates for that condition. >>>> >>>> Best wishes >>>> Gordon >>>> >>>>> Date: Mon, 28 Feb 2011 11:35:42 -0500 >>>>> From: avehna <avhena at="" gmail.com=""> >>>>> To: whuber at embl.de >>>>> Cc: bioconductor at r-project.org >>>>> Subject: Re: [BioC] Detection calls and LIMMA >>>>> >>>>> Dear Wolfgang, >>>>> >>>>> Thank you for your response, I agree with you. I will read the >>>>> paper now... >>>>> >>>>> Best Regards, >>>>> Avhena >>>>> >>>>> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at="" embl.de=""> >>>>> wrote: >>>>> >>>>>> Hi Avhena >>>>>> >>>>>> it is not required, but properly applied filtering can increase >>>>>> detection >>>>>> power in your experiment while still controlling type-I error (false >>>>>> positives). The example you mention seems to be one that you want >>>>>> to keep >>>>>> though, since it is a good candidate for being up-regulated in the >>>>>> Treatment >>>>>> condition. One possibly reasonable criterion would be, e.g., to >>>>>> filter out >>>>>> all probesets that are called 'Absent' on all arrays. Some further >>>>>> discussion on the topic is also here: >>>>>> >>>>>> [1] Bourgon, Gentleman and Huber. Independent filtering increases >>>>>> detection >>>>>> power for high-throughput experiments. PNAS, 107(21):9546-9551, >>>>>> >>>>>> Best wishes >>>>>> Wolfgang >>>>>> >>>>>> >>>>>> Il Feb/28/11 6:51 AM, avehna ha scritto: >>>>>> >>>>>>> Hi All, >>>>>>> >>>>>>> I have a basic question. Is it required to filter the microarray >>>>>>> data >>>>>>> based >>>>>>> on the detection calls (A/M/P) before analyzing it with LIMMA? >>>>>>> >>>>>>> What if I have the following scenario (for example): >>>>>>> >>>>>>> Control Control Control Treatment >>>>>>> Treatment Treatment >>>>>>> 1367813_at A A P >>>>>>> P P P >>>>>>> >>>>>>> Please note that this gene is just "present/detected" once in the >>>>>>> Control, >>>>>>> but it is present in all the replicates of the treatment. In this >>>>>>> case: >>>>>>> what >>>>>>> would be the right thing to do? To eliminate it from the analysis >>>>>>> or keep >>>>>>> it >>>>>>> and consider it up or down depending on the signal of the >>>>>>> treatment? >>>>>>> >>>>>>> Thank a lot! >>>>>>> Avhena >>>> >>>> ______________________________________________________________________ >>>> The information in this email is confidential and >>>> intend...{{dropped: 4}} >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at r-project.org >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >
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