I try to send this message To Gordon Smyth at smyth at vehi,edu.au but it bounced back, so here it is to r-help I am trying to use limma, just downloaded it from CRAN. I use R 2.0.1 on Win XP see the following:> library(RODBC) > chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls") > dd <- sqlFetch(chan1,"Raw") # all data 12000 > # > nzw <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$NZW1T,dd$NZW2T,dd$NZW3T) > akr <- cbind(dd$AKR1C,dd$AKR2C,dd$AKR3C,dd$AKR1T,dd$AKR2T,dd$AKR3T) > bas <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$AKR1C,dd$AKR2C,dd$AKR3C) > # > design<-matrix(c(1,1,1,1,1,1,0,0,0,1,1,1),ncol=2) > fit1 <- lmFit(nzw,design) > fit1 <- eBayes(fit1) > topTable(fit1,adjust="fdr",number=5)M t P.Value B 12222 3679.480 121.24612 7.828493e-06 -4.508864 1903 3012.405 118.32859 7.828493e-06 -4.508866 9068 1850.232 92.70893 1.178902e-05 -4.508889 10635 2843.534 91.99336 1.178902e-05 -4.508890 561 18727.858 90.17085 1.178902e-05 -4.508893> # > fit2 <- lmFit(akr,design) > fit2 <- eBayes(fit2) > topTable(fit2,adjust="fdr",number=5)M t P.Value B 88 1426.738 80.48058 5.839462e-05 -4.510845 1964 36774.167 73.05580 5.839462e-05 -4.510861 5854 7422.578 68.60316 5.839462e-05 -4.510874 11890 1975.316 66.54480 5.839462e-05 -4.510880 9088 2696.952 64.16343 5.839462e-05 -4.510889> # > fit3 <- lmFit(bas,design) > fit3 <- eBayes(fit3) > topTable(fit3,adjust="fdr",number=5)M t P.Value B 6262 1415.088 100.78933 2.109822e-05 -4.521016 5660 1913.479 96.40903 2.109822e-05 -4.521020 11900 4458.489 94.30738 2.109822e-05 -4.521022 9358 1522.330 80.46641 3.346749e-05 -4.521041 11773 1784.483 73.76620 3.346749e-05 -4.521053> # Now lets do all together in Anova > # > all <- cbind(nzw,akr) > ts <- c(1,1,1,2,2,2,3,3,3,4,4,4) > ts <- as.factor(ts) > levels(ts) <- c("nzwC","nzwT","akrC","akrT") > design <- model.matrix(~0+ts) > colnames(design) <- levels(ts) > fit4 <- lmFit(all,design) > cont.matrix <- makeContrasts(+ Baseline = akrC - nzwC, + NZW_Smk = nzwT - nzwC, + AKR_Smk = akrT - akrC, + Diff = (akrT - akrC) - (nzwT - nzwC), + levels=design)> fit42 <- contrasts.fit(fit4,cont.matrix) > fit42 <- eBayes(fit42) > # > topTable(fit42,coef="Baseline",adjust="fdr",number=5)M t P.Value B 3189 942.0993 13.57485 0.004062283 -4.528799 8607 2634.1826 11.23476 0.006913442 -4.530338 10242 -942.2860 -10.99253 0.006913442 -4.530551 283 -609.0831 -10.79354 0.006913442 -4.530735 3224 -1564.2572 -10.19429 0.008089034 -4.531351 ---------------------------------------------------- ------------- Shouldn't this be equal to fit1 above? ----------------------------------------------------> topTable(fit42,coef="NZW_Smk",adjust="fdr",number=5)M t P.Value B 7724 -246.5956 -8.687324 0.1615395 -4.591133 1403 -307.8660 -7.063312 0.4066814 -4.591363 3865 -253.4899 -6.585582 0.4598217 -4.591457 3032 -509.2413 -5.841901 0.8294166 -4.591640 2490 -240.3259 -5.338679 0.9997975 -4.591795 ---------------------------------------------------- ------------- Shouldn't this be equal to fit2 above? ------------- The P.Value are unreal!! ----------------------------------------------------> topTable(fit42,coef="AKR_Smk",adjust="fdr",number=5)M t P.Value B 11547 151.6622 6.380978 0.917470 -4.595085 12064 324.0851 6.337235 0.917470 -4.595085 6752 964.5478 5.858994 0.952782 -4.595086 10251 152.7587 5.339843 0.952782 -4.595087 1440 189.6056 4.933151 0.952782 -4.595089 ---------------------------------------------------- ------------- Shouldn't this be equal to fit3 above? ------------- The P.Value are unreal!! ----------------------------------------------------> topTable(fit42,coef="Diff",adjust="fdr",number=5)M t P.Value B 7724 302.6892 7.540195 0.4102211 -4.593201 1403 419.4962 6.805495 0.4102211 -4.593265 10251 270.5269 6.686796 0.4102211 -4.593277 3270 409.8391 6.414966 0.4192042 -4.593307 10960 -511.4711 -5.469247 0.9652171 -4.593435> # >So the results I get from just pairwise comparisons are very significant, but when I try the Anova way, the significance completely dissapears. Am I doing something completely wrong? This is data from Affimetrix mouse chips. Thanks for any help Heberto Ghezzo Ph.D. Meakins-Christie Labs McGill University Montreal - Canada
Could you give a bit more detail about your experimental design? You're using affy, so you're working with single channel data - so nzw, akr and bas all have six arrays? -----Original Message----- From: r-help-bounces at stat.math.ethz.ch on behalf of r.ghezzo at staff.mcgill.ca Sent: Mon 12/20/2004 8:45 PM To: r-help at stat.math.ethz.ch Cc: Subject: [R] problems with limma I try to send this message To Gordon Smyth at smyth at vehi,edu.au but it bounced back, so here it is to r-help I am trying to use limma, just downloaded it from CRAN. I use R 2.0.1 on Win XP see the following:> library(RODBC) > chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls") > dd <- sqlFetch(chan1,"Raw") # all data 12000 > # > nzw <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$NZW1T,dd$NZW2T,dd$NZW3T) > akr <- cbind(dd$AKR1C,dd$AKR2C,dd$AKR3C,dd$AKR1T,dd$AKR2T,dd$AKR3T) > bas <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$AKR1C,dd$AKR2C,dd$AKR3C) > # > design<-matrix(c(1,1,1,1,1,1,0,0,0,1,1,1),ncol=2) > fit1 <- lmFit(nzw,design) > fit1 <- eBayes(fit1) > topTable(fit1,adjust="fdr",number=5)M t P.Value B 12222 3679.480 121.24612 7.828493e-06 -4.508864 1903 3012.405 118.32859 7.828493e-06 -4.508866 9068 1850.232 92.70893 1.178902e-05 -4.508889 10635 2843.534 91.99336 1.178902e-05 -4.508890 561 18727.858 90.17085 1.178902e-05 -4.508893> # > fit2 <- lmFit(akr,design) > fit2 <- eBayes(fit2) > topTable(fit2,adjust="fdr",number=5)M t P.Value B 88 1426.738 80.48058 5.839462e-05 -4.510845 1964 36774.167 73.05580 5.839462e-05 -4.510861 5854 7422.578 68.60316 5.839462e-05 -4.510874 11890 1975.316 66.54480 5.839462e-05 -4.510880 9088 2696.952 64.16343 5.839462e-05 -4.510889> # > fit3 <- lmFit(bas,design) > fit3 <- eBayes(fit3) > topTable(fit3,adjust="fdr",number=5)M t P.Value B 6262 1415.088 100.78933 2.109822e-05 -4.521016 5660 1913.479 96.40903 2.109822e-05 -4.521020 11900 4458.489 94.30738 2.109822e-05 -4.521022 9358 1522.330 80.46641 3.346749e-05 -4.521041 11773 1784.483 73.76620 3.346749e-05 -4.521053> # Now lets do all together in Anova > # > all <- cbind(nzw,akr) > ts <- c(1,1,1,2,2,2,3,3,3,4,4,4) > ts <- as.factor(ts) > levels(ts) <- c("nzwC","nzwT","akrC","akrT") > design <- model.matrix(~0+ts) > colnames(design) <- levels(ts) > fit4 <- lmFit(all,design) > cont.matrix <- makeContrasts(+ Baseline = akrC - nzwC, + NZW_Smk = nzwT - nzwC, + AKR_Smk = akrT - akrC, + Diff = (akrT - akrC) - (nzwT - nzwC), + levels=design)> fit42 <- contrasts.fit(fit4,cont.matrix) > fit42 <- eBayes(fit42) > # > topTable(fit42,coef="Baseline",adjust="fdr",number=5)M t P.Value B 3189 942.0993 13.57485 0.004062283 -4.528799 8607 2634.1826 11.23476 0.006913442 -4.530338 10242 -942.2860 -10.99253 0.006913442 -4.530551 283 -609.0831 -10.79354 0.006913442 -4.530735 3224 -1564.2572 -10.19429 0.008089034 -4.531351 ---------------------------------------------------- ------------- Shouldn't this be equal to fit1 above? ----------------------------------------------------> topTable(fit42,coef="NZW_Smk",adjust="fdr",number=5)M t P.Value B 7724 -246.5956 -8.687324 0.1615395 -4.591133 1403 -307.8660 -7.063312 0.4066814 -4.591363 3865 -253.4899 -6.585582 0.4598217 -4.591457 3032 -509.2413 -5.841901 0.8294166 -4.591640 2490 -240.3259 -5.338679 0.9997975 -4.591795 ---------------------------------------------------- ------------- Shouldn't this be equal to fit2 above? ------------- The P.Value are unreal!! ----------------------------------------------------> topTable(fit42,coef="AKR_Smk",adjust="fdr",number=5)M t P.Value B 11547 151.6622 6.380978 0.917470 -4.595085 12064 324.0851 6.337235 0.917470 -4.595085 6752 964.5478 5.858994 0.952782 -4.595086 10251 152.7587 5.339843 0.952782 -4.595087 1440 189.6056 4.933151 0.952782 -4.595089 ---------------------------------------------------- ------------- Shouldn't this be equal to fit3 above? ------------- The P.Value are unreal!! ----------------------------------------------------> topTable(fit42,coef="Diff",adjust="fdr",number=5)M t P.Value B 7724 302.6892 7.540195 0.4102211 -4.593201 1403 419.4962 6.805495 0.4102211 -4.593265 10251 270.5269 6.686796 0.4102211 -4.593277 3270 409.8391 6.414966 0.4192042 -4.593307 10960 -511.4711 -5.469247 0.9652171 -4.593435> # >So the results I get from just pairwise comparisons are very significant, but when I try the Anova way, the significance completely dissapears. Am I doing something completely wrong? This is data from Affimetrix mouse chips. Thanks for any help Heberto Ghezzo Ph.D. Meakins-Christie Labs McGill University Montreal - Canada ______________________________________________ R-help at stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
It probably bounces because the email address is incorrect (it should be a 'w', not a 'v'). /Henrik Bengtsson> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of > r.ghezzo at staff.mcgill.ca > Sent: Monday, December 20, 2004 9:45 PM > To: r-help at stat.math.ethz.ch > Subject: [R] problems with limma > > > I try to send this message To Gordon Smyth at > smyth at vehi,edu.au but it bounced back, so here it is to r-help > > I am trying to use limma, just downloaded it from CRAN. I use > R 2.0.1 on Win XP see the following: > > library(RODBC) > > chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls") > > dd <- sqlFetch(chan1,"Raw") # all data 12000 > > # > > nzw <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$NZW1T,dd$NZW2T,dd$NZW3T) > > akr <- cbind(dd$AKR1C,dd$AKR2C,dd$AKR3C,dd$AKR1T,dd$AKR2T,dd$AKR3T) > > bas <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$AKR1C,dd$AKR2C,dd$AKR3C) > > # > > design<-matrix(c(1,1,1,1,1,1,0,0,0,1,1,1),ncol=2) > > fit1 <- lmFit(nzw,design) > > fit1 <- eBayes(fit1) > > topTable(fit1,adjust="fdr",number=5) > M t P.Value B > 12222 3679.480 121.24612 7.828493e-06 -4.508864 > 1903 3012.405 118.32859 7.828493e-06 -4.508866 > 9068 1850.232 92.70893 1.178902e-05 -4.508889 > 10635 2843.534 91.99336 1.178902e-05 -4.508890 > 561 18727.858 90.17085 1.178902e-05 -4.508893 > > # > > fit2 <- lmFit(akr,design) > > fit2 <- eBayes(fit2) > > topTable(fit2,adjust="fdr",number=5) > M t P.Value B > 88 1426.738 80.48058 5.839462e-05 -4.510845 > 1964 36774.167 73.05580 5.839462e-05 -4.510861 > 5854 7422.578 68.60316 5.839462e-05 -4.510874 > 11890 1975.316 66.54480 5.839462e-05 -4.510880 > 9088 2696.952 64.16343 5.839462e-05 -4.510889 > > # > > fit3 <- lmFit(bas,design) > > fit3 <- eBayes(fit3) > > topTable(fit3,adjust="fdr",number=5) > M t P.Value B > 6262 1415.088 100.78933 2.109822e-05 -4.521016 > 5660 1913.479 96.40903 2.109822e-05 -4.521020 > 11900 4458.489 94.30738 2.109822e-05 -4.521022 > 9358 1522.330 80.46641 3.346749e-05 -4.521041 > 11773 1784.483 73.76620 3.346749e-05 -4.521053 > > # Now lets do all together in Anova > > # > > all <- cbind(nzw,akr) > > ts <- c(1,1,1,2,2,2,3,3,3,4,4,4) > > ts <- as.factor(ts) > > levels(ts) <- c("nzwC","nzwT","akrC","akrT") > > design <- model.matrix(~0+ts) > > colnames(design) <- levels(ts) > > fit4 <- lmFit(all,design) > > cont.matrix <- makeContrasts( > + Baseline = akrC - nzwC, > + NZW_Smk = nzwT - nzwC, > + AKR_Smk = akrT - akrC, > + Diff = (akrT - akrC) - (nzwT - nzwC), > + levels=design) > > fit42 <- contrasts.fit(fit4,cont.matrix) > > fit42 <- eBayes(fit42) > > # > > topTable(fit42,coef="Baseline",adjust="fdr",number=5) > M t P.Value B > 3189 942.0993 13.57485 0.004062283 -4.528799 > 8607 2634.1826 11.23476 0.006913442 -4.530338 > 10242 -942.2860 -10.99253 0.006913442 -4.530551 > 283 -609.0831 -10.79354 0.006913442 -4.530735 > 3224 -1564.2572 -10.19429 0.008089034 -4.531351 > ---------------------------------------------------- > ------------- Shouldn't this be equal to fit1 above? > ---------------------------------------------------- > > topTable(fit42,coef="NZW_Smk",adjust="fdr",number=5) > M t P.Value B > 7724 -246.5956 -8.687324 0.1615395 -4.591133 > 1403 -307.8660 -7.063312 0.4066814 -4.591363 > 3865 -253.4899 -6.585582 0.4598217 -4.591457 > 3032 -509.2413 -5.841901 0.8294166 -4.591640 > 2490 -240.3259 -5.338679 0.9997975 -4.591795 > ---------------------------------------------------- > ------------- Shouldn't this be equal to fit2 above? > ------------- The P.Value are unreal!! > ---------------------------------------------------- > > topTable(fit42,coef="AKR_Smk",adjust="fdr",number=5) > M t P.Value B > 11547 151.6622 6.380978 0.917470 -4.595085 > 12064 324.0851 6.337235 0.917470 -4.595085 > 6752 964.5478 5.858994 0.952782 -4.595086 > 10251 152.7587 5.339843 0.952782 -4.595087 > 1440 189.6056 4.933151 0.952782 -4.595089 > ---------------------------------------------------- > ------------- Shouldn't this be equal to fit3 above? > ------------- The P.Value are unreal!! > ---------------------------------------------------- > > topTable(fit42,coef="Diff",adjust="fdr",number=5) > M t P.Value B > 7724 302.6892 7.540195 0.4102211 -4.593201 > 1403 419.4962 6.805495 0.4102211 -4.593265 > 10251 270.5269 6.686796 0.4102211 -4.593277 > 3270 409.8391 6.414966 0.4192042 -4.593307 > 10960 -511.4711 -5.469247 0.9652171 -4.593435 > > # > > > So the results I get from just pairwise comparisons are very > significant, but when I try the Anova way, the significance > completely dissapears. Am I doing something completely wrong? > This is data from Affimetrix mouse chips. Thanks for any help > Heberto Ghezzo Ph.D. Meakins-Christie Labs McGill University > Montreal - Canada > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > >