Most stable gene pairs in array experiment
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David ▴ 860
@david-3335
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Hi, I have the following matrix with normalized log2 values: CondA CondB CondC CondD CondE geneA -6.19 -5.74 -5.82 -5 -5.59 geneB -6.33 -5.32 -5.6 -4.88 -5.39 geneC -6.15 -6.07 -5.6 -4.88 -5.9 geneD -6.57 -6.11 -6.36 -5.36 -5.96 geneD -6.74 -6.2 -5.49 -5.35 -5.95 geneE -6.75 -6.24 -5.73 -5.63 -6.02 Created as follows: geneA<-c(-6.19, -5.74, -5.82, -5, -5.59) geneB<-c(-6.33, -5.32, -5.6, -4.88, -5.39) geneC<-c(-6.15, -6.07, -5.6, -4.88, -5.9) geneD<-c(-6.57, -6.11, -6.36, -5.36, -5.96) geneD<-c(-6.74, -6.2, -5.49, -5.35, -5.95) geneE<-c(-6.75, -6.24, -5.73, -5.63, -6.02) mygenes<-rbind(geneA, geneB, geneC, geneD, geneE) colnames(mygenes)<-c("CondA", "CondB", "CondC", "CondD", "CondE") I'm looking for most stable pair genes across conditions. I'm not looking for individual gene variance but really for most stable pairs ratios. For e.g What is the variance of geneA vs geneB across all conditions. What is the most stable pair ? Any help would be appreciated. david
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@anna-freni-sterrantino-2847
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Hi David, not sure what do you mean with stable, but you might be interested in correlation, a=matrix(sample(1:100),4,5) rownames(a)=paste("gene", letters[1:4]) colnames(a)=paste("cond", letters[1:5]) >a cond a cond b cond c cond d cond e gene a 95 31 3 9 93 gene b 16 67 83 81 86 gene c 59 79 44 77 39 gene d 36 92 41 57 66 > cor(t(a)) gene a gene b gene c gene d gene a 1.0000000 -0.5362894 -0.3830295 -0.1109239 gene b -0.5362894 1.0000000 -0.1710537 0.3790986 gene c -0.3830295 -0.1710537 1.0000000 0.4612277 gene d -0.1109239 0.3790986 0.4612277 1.0000000 and then across all the pairs the most correlated will be those that have a correlation value that is close to |1|. The correlation tells you how much close are two variables in terms of linear relationship. Hope this helps. Cheers A Anna Freni Sterrantino Ph.D Student Department of Statistics University of Bologna, Italy via Belle Arti 41, 40124 BO. ________________________________ Da: David martin <vilanew@gmail.com> A: bioconductor@stat.math.ethz.ch Inviato: Lun 19 ottobre 2009, 10:37:08 Oggetto: [BioC] Most stable gene pairs in array experiment Hi, I have the following matrix with normalized log2 values: CondA CondB CondC CondD CondE geneA -6.19 -5.74 -5.82 -5 -5.59 geneB -6.33 -5.32 -5.6 -4.88 -5.39 geneC -6.15 -6.07 -5.6 -4.88 -5.9 geneD -6.57 -6.11 -6.36 -5.36 -5.96 geneD -6.74 -6.2 -5.49 -5.35 -5.95 geneE -6.75 -6.24 -5.73 -5.63 -6.02 Created as follows: geneA<-c(-6.19, -5.74, -5.82, -5, -5.59) geneB<-c(-6.33, -5.32, -5.6, -4.88, -5.39) geneC<-c(-6.15, -6.07, -5.6, -4.88, -5.9) geneD<-c(-6.57, -6.11, -6.36, -5.36, -5.96) geneD<-c(-6.74, -6.2, -5.49, -5.35, -5.95) geneE<-c(-6.75, -6.24, -5.73, -5.63, -6.02) mygenes<-rbind(geneA, geneB, geneC, geneD, geneE) colnames(mygenes)<-c("CondA", "CondB", "CondC", "CondD", "CondE") I'm looking for most stable pair genes across conditions. I'm not looking for individual gene variance but really for most stable pairs ratios. For e.g What is the variance of geneA vs geneB across all conditions. What is the most stable pair ? Any help would be appreciated. david _______________________________________________ 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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It sounds to me like you are looking for var(geneA-geneB). --Naomi At 05:54 AM 10/19/2009, anna freni sterrantino wrote: >Hi David, >not sure what do you mean with stable, >but you might be interested in correlation, > >a=matrix(sample(1:100),4,5) >rownames(a)=paste("gene", letters[1:4]) >colnames(a)=paste("cond", letters[1:5]) > >a >cond a cond b cond c cond d cond e >gene a 95 31 3 9 93 >gene b 16 67 83 81 86 >gene c 59 79 44 77 39 >gene d 36 92 41 57 66 > > cor(t(a)) > gene a gene b gene c gene d >gene a 1.0000000 -0.5362894 -0.3830295 -0.1109239 >gene b -0.5362894 1.0000000 -0.1710537 0.3790986 >gene c -0.3830295 -0.1710537 1.0000000 0.4612277 >gene d -0.1109239 0.3790986 0.4612277 1.0000000 > >and then across all the pairs the most correlated will be those >that have a correlation value that is close to |1|. >The correlation tells you how much close are two variables in terms >of linear relationship. > >Hope this helps. >Cheers > >A > > > >Anna Freni Sterrantino >Ph.D Student >Department of Statistics >University of Bologna, Italy >via Belle Arti 41, 40124 BO. > > > > >________________________________ >Da: David martin <vilanew at="" gmail.com=""> >A: bioconductor at stat.math.ethz.ch >Inviato: Lun 19 ottobre 2009, 10:37:08 >Oggetto: [BioC] Most stable gene pairs in array experiment > >Hi, >I have the following matrix with normalized log2 values: >CondA CondB CondC CondD CondE >geneA -6.19 -5.74 -5.82 -5 -5.59 >geneB -6.33 -5.32 -5.6 -4.88 -5.39 >geneC -6.15 -6.07 -5.6 -4.88 -5.9 >geneD -6.57 -6.11 -6.36 -5.36 -5.96 >geneD -6.74 -6.2 -5.49 -5.35 -5.95 >geneE -6.75 -6.24 -5.73 -5.63 -6.02 > > >Created as follows: >geneA<-c(-6.19, -5.74, -5.82, -5, -5.59) >geneB<-c(-6.33, -5.32, -5.6, -4.88, -5.39) >geneC<-c(-6.15, -6.07, -5.6, -4.88, -5.9) >geneD<-c(-6.57, -6.11, -6.36, -5.36, -5.96) >geneD<-c(-6.74, -6.2, -5.49, -5.35, -5.95) >geneE<-c(-6.75, -6.24, -5.73, -5.63, -6.02) > >mygenes<-rbind(geneA, geneB, geneC, geneD, geneE) >colnames(mygenes)<-c("CondA", "CondB", "CondC", "CondD", >"CondE") > >I'm looking for most stable pair genes across conditions. I'm not >looking for individual gene variance but really for most stable pairs ratios. >For e.g What is the variance of geneA vs geneB across all >conditions. What is the most stable pair ? > >Any help would be appreciated. > >david > >_______________________________________________ >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 > > > > > [[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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Exactly that what i want to do. How could i do that on the whole matrix taking all pairs (or at least one fixed gene ,e.g geneA and compute all var comparisons var(geneA-geneB) var(geneA-geneC) var(geneA-geneC) and see which variance is better ?? thanks Naomi Altman wrote: > It sounds to me like you are looking for var(geneA-geneB). > --Naomi > > At 05:54 AM 10/19/2009, anna freni sterrantino wrote: >> Hi David, >> not sure what do you mean with stable, >> but you might be interested in correlation, >> >> a=matrix(sample(1:100),4,5) >> rownames(a)=paste("gene", letters[1:4]) >> colnames(a)=paste("cond", letters[1:5]) >> >a >> cond a cond b cond c cond d cond e >> gene a 95 31 3 9 93 >> gene b 16 67 83 81 86 >> gene c 59 79 44 77 39 >> gene d 36 92 41 57 66 >> > cor(t(a)) >> gene a gene b gene c gene d >> gene a 1.0000000 -0.5362894 -0.3830295 -0.1109239 >> gene b -0.5362894 1.0000000 -0.1710537 0.3790986 >> gene c -0.3830295 -0.1710537 1.0000000 0.4612277 >> gene d -0.1109239 0.3790986 0.4612277 1.0000000 >> >> and then across all the pairs the most correlated will be those >> that have a correlation value that is close to |1|. >> The correlation tells you how much close are two variables in terms >> of linear relationship. >> >> Hope this helps. >> Cheers >> >> A >> >> >> >> Anna Freni Sterrantino >> Ph.D Student >> Department of Statistics >> University of Bologna, Italy >> via Belle Arti 41, 40124 BO. >> >> >> >> >> ________________________________ >> Da: David martin <vilanew at="" gmail.com=""> >> A: bioconductor at stat.math.ethz.ch >> Inviato: Lun 19 ottobre 2009, 10:37:08 >> Oggetto: [BioC] Most stable gene pairs in array experiment >> >> Hi, >> I have the following matrix with normalized log2 values: >> CondA CondB CondC CondD CondE >> geneA -6.19 -5.74 -5.82 -5 -5.59 >> geneB -6.33 -5.32 -5.6 -4.88 -5.39 >> geneC -6.15 -6.07 -5.6 -4.88 -5.9 >> geneD -6.57 -6.11 -6.36 -5.36 -5.96 >> geneD -6.74 -6.2 -5.49 -5.35 -5.95 >> geneE -6.75 -6.24 -5.73 -5.63 -6.02 >> >> >> Created as follows: >> geneA<-c(-6.19, -5.74, -5.82, -5, -5.59) >> geneB<-c(-6.33, -5.32, -5.6, -4.88, -5.39) >> geneC<-c(-6.15, -6.07, -5.6, -4.88, -5.9) >> geneD<-c(-6.57, -6.11, -6.36, -5.36, -5.96) >> geneD<-c(-6.74, -6.2, -5.49, -5.35, -5.95) >> geneE<-c(-6.75, -6.24, -5.73, -5.63, -6.02) >> >> mygenes<-rbind(geneA, geneB, geneC, geneD, geneE) >> colnames(mygenes)<-c("CondA", "CondB", "CondC", "CondD", >> "CondE") >> >> I'm looking for most stable pair genes across conditions. I'm not >> looking for individual gene variance but really for most stable pairs >> ratios. >> For e.g What is the variance of geneA vs geneB across all conditions. >> What is the most stable pair ? >> >> Any help would be appreciated. >> >> david >> >> _______________________________________________ >> 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 >> >> >> >> >> [[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 > > _______________________________________________ > 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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Exactly that what i want to do. How could i do that on the whole matrix taking all pairs (or at least one fixed gene ,e.g geneA and compute all var comparisons var(geneA-geneB) var(geneA-geneC) var(geneA-geneC) and see which variance is better ?? thanks Naomi Altman wrote: > It sounds to me like you are looking for var(geneA-geneB). > --Naomi > > At 05:54 AM 10/19/2009, anna freni sterrantino wrote: >> Hi David, >> not sure what do you mean with stable, >> but you might be interested in correlation, >> >> a=matrix(sample(1:100),4,5) >> rownames(a)=paste("gene", letters[1:4]) >> colnames(a)=paste("cond", letters[1:5]) >> >a >> cond a cond b cond c cond d cond e >> gene a 95 31 3 9 93 >> gene b 16 67 83 81 86 >> gene c 59 79 44 77 39 >> gene d 36 92 41 57 66 >> > cor(t(a)) >> gene a gene b gene c gene d >> gene a 1.0000000 -0.5362894 -0.3830295 -0.1109239 >> gene b -0.5362894 1.0000000 -0.1710537 0.3790986 >> gene c -0.3830295 -0.1710537 1.0000000 0.4612277 >> gene d -0.1109239 0.3790986 0.4612277 1.0000000 >> >> and then across all the pairs the most correlated will be those >> that have a correlation value that is close to |1|. >> The correlation tells you how much close are two variables in terms >> of linear relationship. >> >> Hope this helps. >> Cheers >> >> A >> >> >> >> Anna Freni Sterrantino >> Ph.D Student >> Department of Statistics >> University of Bologna, Italy >> via Belle Arti 41, 40124 BO. >> >> >> >> >> ________________________________ >> Da: David martin <vilanew at="" gmail.com=""> >> A: bioconductor at stat.math.ethz.ch >> Inviato: Lun 19 ottobre 2009, 10:37:08 >> Oggetto: [BioC] Most stable gene pairs in array experiment >> >> Hi, >> I have the following matrix with normalized log2 values: >> CondA CondB CondC CondD CondE >> geneA -6.19 -5.74 -5.82 -5 -5.59 >> geneB -6.33 -5.32 -5.6 -4.88 -5.39 >> geneC -6.15 -6.07 -5.6 -4.88 -5.9 >> geneD -6.57 -6.11 -6.36 -5.36 -5.96 >> geneD -6.74 -6.2 -5.49 -5.35 -5.95 >> geneE -6.75 -6.24 -5.73 -5.63 -6.02 >> >> >> Created as follows: >> geneA<-c(-6.19, -5.74, -5.82, -5, -5.59) >> geneB<-c(-6.33, -5.32, -5.6, -4.88, -5.39) >> geneC<-c(-6.15, -6.07, -5.6, -4.88, -5.9) >> geneD<-c(-6.57, -6.11, -6.36, -5.36, -5.96) >> geneD<-c(-6.74, -6.2, -5.49, -5.35, -5.95) >> geneE<-c(-6.75, -6.24, -5.73, -5.63, -6.02) >> >> mygenes<-rbind(geneA, geneB, geneC, geneD, geneE) >> colnames(mygenes)<-c("CondA", "CondB", "CondC", "CondD", >> "CondE") >> >> I'm looking for most stable pair genes across conditions. I'm not >> looking for individual gene variance but really for most stable pairs >> ratios. >> For e.g What is the variance of geneA vs geneB across all conditions. >> What is the most stable pair ? >> >> Any help would be appreciated. >> >> david >> >> _______________________________________________ >> 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 >> >> >> >> >> [[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 > > _______________________________________________ > 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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Hi, you can try something like this or use two for loops: > apply (mygenes, 1 , function(row) { apply (mygenes, 1, function(x) { var(row-x) } ) } ) geneA geneB geneC geneD geneE geneA 0.00000 0.04108 0.06397 0.12217 0.08233 geneB 0.04108 0.00000 0.15543 0.12807 0.09365 geneC 0.06397 0.15543 0.00000 0.08628 0.08903 geneD 0.12217 0.12807 0.08628 0.00000 0.01517 geneE 0.08233 0.09365 0.08903 0.01517 0.00000 Cheers Michael Am 20.10.2009 um 10:09 schrieb David martin: > Exactly that what i want to do. > How could i do that on the whole matrix taking all pairs (or at > least one fixed gene ,e.g geneA and compute all var comparisons > var(geneA-geneB) var(geneA-geneC) var(geneA-geneC) and see which > variance is better ?? > thanks > > Naomi Altman wrote: >> It sounds to me like you are looking for var(geneA-geneB). >> --Naomi >> At 05:54 AM 10/19/2009, anna freni sterrantino wrote: >>> Hi David, >>> not sure what do you mean with stable, >>> but you might be interested in correlation, >>> >>> a=matrix(sample(1:100),4,5) >>> rownames(a)=paste("gene", letters[1:4]) >>> colnames(a)=paste("cond", letters[1:5]) >>> >a >>> cond a cond b cond c cond d cond e >>> gene a 95 31 3 9 93 >>> gene b 16 67 83 81 86 >>> gene c 59 79 44 77 39 >>> gene d 36 92 41 57 66 >>> > cor(t(a)) >>> gene a gene b gene c gene d >>> gene a 1.0000000 -0.5362894 -0.3830295 -0.1109239 >>> gene b -0.5362894 1.0000000 -0.1710537 0.3790986 >>> gene c -0.3830295 -0.1710537 1.0000000 0.4612277 >>> gene d -0.1109239 0.3790986 0.4612277 1.0000000 >>> >>> and then across all the pairs the most correlated will be those >>> that have a correlation value that is close to |1|. >>> The correlation tells you how much close are two variables in terms >>> of linear relationship. >>> >>> Hope this helps. >>> Cheers >>> >>> A >>> >>> >>> >>> Anna Freni Sterrantino >>> Ph.D Student >>> Department of Statistics >>> University of Bologna, Italy >>> via Belle Arti 41, 40124 BO. >>> >>> >>> >>> >>> ________________________________ >>> Da: David martin <vilanew at="" gmail.com=""> >>> A: bioconductor at stat.math.ethz.ch >>> Inviato: Lun 19 ottobre 2009, 10:37:08 >>> Oggetto: [BioC] Most stable gene pairs in array experiment >>> >>> Hi, >>> I have the following matrix with normalized log2 values: >>> CondA CondB CondC CondD CondE >>> geneA -6.19 -5.74 -5.82 -5 -5.59 >>> geneB -6.33 -5.32 -5.6 -4.88 -5.39 >>> geneC -6.15 -6.07 -5.6 -4.88 -5.9 >>> geneD -6.57 -6.11 -6.36 -5.36 -5.96 >>> geneD -6.74 -6.2 -5.49 -5.35 -5.95 >>> geneE -6.75 -6.24 -5.73 -5.63 -6.02 >>> >>> >>> Created as follows: >>> geneA<-c(-6.19, -5.74, -5.82, -5, -5.59) >>> geneB<-c(-6.33, -5.32, -5.6, -4.88, -5.39) >>> geneC<-c(-6.15, -6.07, -5.6, -4.88, -5.9) >>> geneD<-c(-6.57, -6.11, -6.36, -5.36, -5.96) >>> geneD<-c(-6.74, -6.2, -5.49, -5.35, -5.95) >>> geneE<-c(-6.75, -6.24, -5.73, -5.63, -6.02) >>> >>> mygenes<-rbind(geneA, geneB, geneC, geneD, geneE) >>> colnames(mygenes)<-c("CondA", "CondB", "CondC", "CondD", >>> "CondE") >>> >>> I'm looking for most stable pair genes across conditions. I'm not >>> looking for individual gene variance but really for most stable >>> pairs ratios. >>> For e.g What is the variance of geneA vs geneB across all >>> conditions. What is the most stable pair ? >>> >>> Any help would be appreciated. >>> >>> david >>> >>> _______________________________________________ >>> 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 >>> >>> >>> >>> >>> [[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 >> _______________________________________________ >> 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 >> > > _______________________________________________ > 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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2009/10/20 Michael Dondrup <michael.dondrup at="" bccs.uib.no="">: > Hi, > > you can ?try something like this or use two for loops: > > >> apply (mygenes, 1 , function(row) { apply (mygenes, 1, function(x) { >> var(row-x) ?} ) } ) > > ? ? ? ?geneA ? geneB ? geneC ? geneD ? geneE > geneA 0.00000 0.04108 0.06397 0.12217 0.08233 > geneB 0.04108 0.00000 0.15543 0.12807 0.09365 > geneC 0.06397 0.15543 0.00000 0.08628 0.08903 > geneD 0.12217 0.12807 0.08628 0.00000 0.01517 > geneE 0.08233 0.09365 0.08903 0.01517 0.00000 > > > Cheers > Michael Hi, I am following the discussion and I'm finding very interesting. Congratulations! After this, I could compare the two genes, two-by-two, and I could conclude that the pair with minor variance are the two most stable genes of all? Is this genes appropriated for qPCR internal control? Or am I totally wrong here? Thank you very much! -- Marcelo Luiz de Laia Universidade do Estado de Santa Catarina UDESC - www.cav.udesc.br Lages - SC - Brazil Linux user number 487797
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Hi, one can of course compute a lot of things the question is if that makes sense. The term 'stability' is as far as i know not well defined in the statistical sense, however there is a definition of numerical stability for algorithms. http://en.wikipedia.org/wiki/Numerical_stability Therefor, use of 'stability' in this context is not too helpful. If you have multivariate random variables (genesA,...) you can of course define, that you search for the minimum of of the variance estimate of the difference (e.g. var(geneA - geneB)). Thereby creating a new random variable from the difference of two variables Z= X-Y. If application of the variance estimate makes sense here, I cannot know. If you need gene pairs that somewhat 'always react in the way', e.g. for qPCR correlation might be a more appropriate concept, as mentioned by others, and it's well defined. If you look only at the difference between gene pairs, the result can be influenced by the individual variance of both genes, even if they otherwise have the same behaviour under each condition. Furthermore, one could possibly apply a correlation test (corr.test) and select those genes which are most significant. But this will always depend on the desired application which is not clear to me. One could also choose 'housekeeping' genes by prior knowledge. Maybe there are even better methods for selecting controls for qPCR experiments from other data in the literature? Hope that helps even though it's maybe a bit off-topic. Michael Am 20.10.2009 um 14:30 schrieb Marcelo Laia: > 2009/10/20 Michael Dondrup <michael.dondrup at="" bccs.uib.no="">: >> Hi, >> >> you can try something like this or use two for loops: >> >> >>> apply (mygenes, 1 , function(row) { apply (mygenes, 1, function(x) { >>> var(row-x) } ) } ) >> >> geneA geneB geneC geneD geneE >> geneA 0.00000 0.04108 0.06397 0.12217 0.08233 >> geneB 0.04108 0.00000 0.15543 0.12807 0.09365 >> geneC 0.06397 0.15543 0.00000 0.08628 0.08903 >> geneD 0.12217 0.12807 0.08628 0.00000 0.01517 >> geneE 0.08233 0.09365 0.08903 0.01517 0.00000 >> >> >> Cheers >> Michael > > Hi, > > I am following the discussion and I'm finding very interesting. > Congratulations! > > After this, I could compare the two genes, two-by-two, and I could > conclude that the pair with minor variance are the two most stable > genes of all? > > Is this genes appropriated for qPCR internal control? Or am I totally > wrong here? > > Thank you very much! > > -- > Marcelo Luiz de Laia > Universidade do Estado de Santa Catarina > UDESC - www.cav.udesc.br > Lages - SC - Brazil > Linux user number 487797 > > _______________________________________________ > 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 Michael Dondrup, Ph.D. Bergen Center for Computational Science Computational Biology Unit Unifob AS - Thorm?hlensgate 55, N-5008 Bergen, Norway
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Naomi Altman ★ 6.0k
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You would conclude that the pairs of genes with very low variance track each other closely in the samples. Since the analysis is on the log-scale, this means that the fold ratio is stable. It does not mean that the two genes do not vary. For that, you would want to compute the gene-wise variance. Naomi At 08:30 AM 10/20/2009, Marcelo Laia wrote: >2009/10/20 Michael Dondrup <michael.dondrup at="" bccs.uib.no="">: > > Hi, > > > > you can try something like this or use two for loops: > > > > > >> apply (mygenes, 1 , function(row) { apply (mygenes, 1, function(x) { > >> var(row-x) } ) } ) > > > > geneA geneB geneC geneD geneE > > geneA 0.00000 0.04108 0.06397 0.12217 0.08233 > > geneB 0.04108 0.00000 0.15543 0.12807 0.09365 > > geneC 0.06397 0.15543 0.00000 0.08628 0.08903 > > geneD 0.12217 0.12807 0.08628 0.00000 0.01517 > > geneE 0.08233 0.09365 0.08903 0.01517 0.00000 > > > > > > Cheers > > Michael > >Hi, > >I am following the discussion and I'm finding very interesting. >Congratulations! > >After this, I could compare the two genes, two-by-two, and I could >conclude that the pair with minor variance are the two most stable >genes of all? > >Is this genes appropriated for qPCR internal control? Or am I totally >wrong here? > >Thank you very much! > >-- >Marcelo Luiz de Laia >Universidade do Estado de Santa Catarina >UDESC - www.cav.udesc.br >Lages - SC - Brazil >Linux user number 487797 > >_______________________________________________ >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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Hi thanks for the answers and interesting dicussions: Yes, indeed it's the only way i have to look for internal controls. Since i can't find any stable gene across conditions i assume that pairs can be stable (although each single gene can vary across conditions). I expect (geneA-geneB) to be stable (at least is what i will try). I'm not comouting correlations since i'm not looking at all that genes correlate (for e.g geneA could be up regulated and gene B down-regulated), but geneA-geneB might remain stable !!! thanks again, david Naomi Altman wrote: > You would conclude that the pairs of genes with very low variance track > each other closely in the samples. > Since the analysis is on the log-scale, this means that the fold ratio > is stable. It does not mean that the two genes do not vary. For that, > you would want to compute the gene-wise variance. > > Naomi > > At 08:30 AM 10/20/2009, Marcelo Laia wrote: >> 2009/10/20 Michael Dondrup >> <michael.dondrup at="" bccs.uib.no="">: >> > Hi, >> > >> > you can try something like this or use two for loops: >> > >> > >> >> apply (mygenes, 1 , function(row) { apply (mygenes, 1, function(x) { >> >> var(row-x) } ) } ) >> > >> > geneA geneB geneC geneD geneE >> > geneA 0.00000 0.04108 0.06397 0.12217 0.08233 >> > geneB 0.04108 0.00000 0.15543 0.12807 0.09365 >> > geneC 0.06397 0.15543 0.00000 0.08628 0.08903 >> > geneD 0.12217 0.12807 0.08628 0.00000 0.01517 >> > geneE 0.08233 0.09365 0.08903 0.01517 0.00000 >> > >> > >> > Cheers >> > Michael >> >> Hi, >> >> I am following the discussion and I'm finding very interesting. >> Congratulations! >> >> After this, I could compare the two genes, two-by-two, and I could >> conclude that the pair with minor variance are the two most stable >> genes of all? >> >> Is this genes appropriated for qPCR internal control? Or am I totally >> wrong here? >> >> Thank you very much! >> >> -- >> Marcelo Luiz de Laia >> Universidade do Estado de Santa Catarina >> UDESC - www.cav.udesc.br >> Lages - SC - Brazil >> Linux user number 487797 >> >> _______________________________________________ >> 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 > > _______________________________________________ > 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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