filter on Human Gene U133 Plus 2
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Yuan Hao ▴ 240
@yuan-hao-3658
Last seen 10.2 years ago
United States
You might want to look at the 'genefilter' package which filters probe sets based on different requirements, and there is a function to filter out all control probe sets as well. Also, given that there are over 50,000 probe sets on hgu133plus2 array, cut it down to 500-3000 for me sounds too harsh, although I don't think there is a golden-standard about what would be a good number after filtering. It depends on the design and expectations of your experiment. However, for me ~26,000 probe sets afterwards have no harm. Cheers, Yuan On 29 Oct 2010, at 20:42, cstrato wrote: > Dear Naima, > > Usually I prefer to reduce the number of probesets using "median > absolute deviation" as prefilter (see e.g. ?madFilter). This reduces > the number of probesets to about 500-3000 (depending on the cutoff). > Afterwards I use either unitestFilter and fcFilter (see Chapter 5.2 > of xps.pdf), or I use the package "limma" which most people use (see > Appendix A.3 how to create an ExpressionSet for use with limma). > Generally I do not consider MAS5 detection calls to be sufficient > for pre-filtering. > > I am sure that other people can give you a more detailed answer, but > using "moderated t-statistics" is generally a good idea. > > Best regards > Christian > > > On 10/29/10 4:45 PM, Na?ma Oumouhou wrote: >> Dear Christian, >> >> I'm sorry to bother you again : I've got a question about filter on >> Affymetrix Human gene U133 Plus 2 array. >> I would like to find differentiallty expressed genes between 2 >> groups of >> patients (n1=7 and n2=6). >> I have no experience in microarray analyses. >> I read several publications and your xps vignettes but I don't know >> what >> I have to do. >> Some people filtered probesets using Detection MAS5 call:probesets >> that >> aren't expressed in at least one sample using the Detection MAS5 >> algorithm are discarded. >> What do you think about this filter?not tight enough? >> After this filter and with my dataset,I still have 26 495 probesets? >> Is >> it too much? >> Furthermore, in these remaining probesets, there are affymetrixx >> control >> probesets. These probesets have to be removed?At which step? >> After, I use the moderated t-statistics with BH correction. I find no >> differentiallty expressed genes. >> I wonder if I have to reduce the number of probesets with another >> filter >> or with a "Detection filter" tighter? >> Thanks for any help. >> Na?ma >> Saisissez du texte, l'adresse d'un site Web ou importez un document ? >> traduire. <http: translate.google.fr="" ?tr="f&amp;hl=fr"> >> Annuler <http: translate.google.fr="" ?tr="t&amp;hl=fr"> >> > > _______________________________________________ > 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
Microarray probe xps Microarray probe xps • 1.3k views
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@wxumsiumnedu-1819
Last seen 10.2 years ago
Use MAS5, RMA, or other algorithms would be fine. The point is whether the gene filtering is proper. The raw p-values are not changed after filtering. The gene filtering only makes the FDR looking nicer. Different person may report a different FDR for the same gene list even with the same data set because of the different number filtered. If you want to get lower and nicer FDR, filter and reduce the total gene number to a small number. Do you think that is kind of cheating :) Wayne -- On 11/2/2010 4:27 AM, Naïma Oumouhou wrote: > Wayne, > Thank you for your response. > I thought it was a good idea to use the Detection MAS5 algorithm > because I remove genes not expressed or expressed a little. > Best regards > > Le 29/10/2010 19:25, wxu a écrit : >> Naima, >> Why do you need to filter genes? For bad quality or some other >> reasons in your mind? Read my recent paper you may have an idea: >> http://www.ncbi.nlm.nih.gov/pubmed/20846437 >> >> Wayne >> -- >> Naïma Oumouhou wrote: >>> Dear Christian, >>> >>> I'm sorry to bother you again : I've got a question about filter on >>> Affymetrix Human gene U133 Plus 2 array. >>> I would like to find differentiallty expressed genes between 2 >>> groups of patients (n1=7 and n2=6). >>> I have no experience in microarray analyses. >>> I read several publications and your xps vignettes but I don't know >>> what I have to do. >>> Some people filtered probesets using Detection MAS5 call:probesets >>> that aren't expressed in at least one sample using the Detection >>> MAS5 algorithm are discarded. >>> What do you think about this filter?not tight enough? >>> After this filter and with my dataset,I still have 26 495 >>> probesets?Is it too much? >>> Furthermore, in these remaining probesets, there are affymetrixx >>> control probesets. These probesets have to be removed?At which step? >>> After, I use the moderated t-statistics with BH correction. I find >>> no differentiallty expressed genes. >>> I wonder if I have to reduce the number of probesets with another >>> filter or with a "Detection filter" tighter? >>> Thanks for any help. >>> Naïma >>> Saisissez du texte, l'adresse d'un site Web ou importez un document >>> à traduire. <http: translate.google.fr="" ?tr="f&amp;hl=fr"> >>> <http: translate.google.fr="" ?tr="f&amp;hl=fr"> >>> Annuler <http: translate.google.fr="" ?tr="t&amp;hl=fr"> >>> <http: translate.google.fr="" ?tr="t&amp;hl=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 >> >> >> >> > [[alternative HTML version deleted]]
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Il Nov/2/10 2:54 PM, wxu ha scritto: > Use MAS5, RMA, or other algorithms would be fine. The point is whether > the gene filtering is proper. The raw p-values are not changed after > filtering. The gene filtering only makes the FDR looking nicer. > Different person may report a different FDR for the same gene list even > with the same data set because of the different number filtered. If you > want to get lower and nicer FDR, filter and reduce the total gene number > to a small number. Do you think that is kind of cheating :) Hi Wayne, Naima in a nutshell, filtering is able to increase detection power, while maintaining type 1 error, if the filtering criterion is correlated with your test statistic (e.g. t-statistic) under the test's alternative, but independent from it under the null. There are some important technical points on the application of this statement to different concrete test statistics, filtering criteria and type 1 error measures, which are discussed in the paper "Independent filtering increases detection power for high-throughput experiments", http://www.pnas.org/content/107/21/9546.long Best wishes Wolfgang > > > Wayne > -- > > On 11/2/2010 4:27 AM, Na?ma Oumouhou wrote: >> Wayne, >> Thank you for your response. >> I thought it was a good idea to use the Detection MAS5 algorithm >> because I remove genes not expressed or expressed a little. >> Best regards >> >> Le 29/10/2010 19:25, wxu a ?crit : >>> Naima, >>> Why do you need to filter genes? For bad quality or some other >>> reasons in your mind? Read my recent paper you may have an idea: >>> http://www.ncbi.nlm.nih.gov/pubmed/20846437 >>> >>> Wayne >>> -- >>> Na?ma Oumouhou wrote: >>>> Dear Christian, >>>> >>>> I'm sorry to bother you again : I've got a question about filter on >>>> Affymetrix Human gene U133 Plus 2 array. >>>> I would like to find differentiallty expressed genes between 2 >>>> groups of patients (n1=7 and n2=6). >>>> I have no experience in microarray analyses. >>>> I read several publications and your xps vignettes but I don't know >>>> what I have to do. >>>> Some people filtered probesets using Detection MAS5 call:probesets >>>> that aren't expressed in at least one sample using the Detection >>>> MAS5 algorithm are discarded. >>>> What do you think about this filter?not tight enough? >>>> After this filter and with my dataset,I still have 26 495 >>>> probesets?Is it too much? >>>> Furthermore, in these remaining probesets, there are affymetrixx >>>> control probesets. These probesets have to be removed?At which step? >>>> After, I use the moderated t-statistics with BH correction. I find >>>> no differentiallty expressed genes. >>>> I wonder if I have to reduce the number of probesets with another >>>> filter or with a "Detection filter" tighter? >>>> Thanks for any help. >>>> Na?ma >>>> Saisissez du texte, l'adresse d'un site Web ou importez un document >>>> ? traduire.<http: translate.google.fr="" ?tr="f&amp;hl=fr"> >>>> <http: translate.google.fr="" ?tr="f&amp;hl=fr"> >>>> Annuler<http: translate.google.fr="" ?tr="t&amp;hl=fr"> >>>> <http: translate.google.fr="" ?tr="t&amp;hl=fr"> >>>> >>>> >>>> [[alternative HTML version deleted]] >>>> >>>> >>>> ----------------------------------------------------------------- ------- >>>> >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at stat.math.ethz.ch<mailto: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 >>> >>> >>> >>> >> > > > [[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
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@naima-oumouhou-4270
Last seen 10.2 years ago
Wayne, Thank you for your response. I thought it was a good idea to use the Detection MAS5 algorithm because I remove genes not expressed or expressed a little. Best regards Le 29/10/2010 19:25, wxu a écrit : > Naima, > Why do you need to filter genes? For bad quality or some other reasons > in your mind? Read my recent paper you may have an idea: > http://www.ncbi.nlm.nih.gov/pubmed/20846437 > > Wayne > -- > Naïma Oumouhou wrote: >> Dear Christian, >> >> I'm sorry to bother you again : I've got a question about filter on >> Affymetrix Human gene U133 Plus 2 array. >> I would like to find differentiallty expressed genes between 2 groups >> of patients (n1=7 and n2=6). >> I have no experience in microarray analyses. >> I read several publications and your xps vignettes but I don't know >> what I have to do. >> Some people filtered probesets using Detection MAS5 call:probesets >> that aren't expressed in at least one sample using the Detection >> MAS5 algorithm are discarded. >> What do you think about this filter?not tight enough? >> After this filter and with my dataset,I still have 26 495 >> probesets?Is it too much? >> Furthermore, in these remaining probesets, there are affymetrixx >> control probesets. These probesets have to be removed?At which step? >> After, I use the moderated t-statistics with BH correction. I find no >> differentiallty expressed genes. >> I wonder if I have to reduce the number of probesets with another >> filter or with a "Detection filter" tighter? >> Thanks for any help. >> Naïma >> Saisissez du texte, l'adresse d'un site Web ou importez un document à >> traduire. <http: translate.google.fr="" ?tr="f&amp;hl=fr"> >> Annuler <http: translate.google.fr="" ?tr="t&amp;hl=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 > > > > [[alternative HTML version deleted]]
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