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Question: What's the difference of the two approach for two groups in limma?
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gravatar for Ou, Jianhong
7.7 years ago by
Ou, Jianhong1.1k
United States
Ou, Jianhong1.1k wrote:
Hi all, In the usersguide.pdf of limma, there are two approach for two groups (8.4, Two Groups: Common Reference) At first, I thought they are same, but... [code] > sd <- 0.3*sqrt(4/rchisq(100,df=4)) > y <- matrix(rnorm(100*6,sd=sd),100,6) > rownames(y) <- paste("Gene",1:100) > y[1:2,4:6] <- y[1:2,4:6] + 2 > design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1)) > fit <- lmFit(y,design) > fit <- eBayes(fit) > topTable(fit,coef=1,n=20,sort.by="logFC") ID logFC t P.Value adj.P.Val B 33 Gene 33 1.2800739 4.9383702 0.001156486 0.1156486 -0.4485569 23 Gene 23 -0.9290832 -1.6732940 0.133021448 0.9913757 -4.9814041 27 Gene 27 0.8616516 3.3965657 0.009487765 0.4743882 -2.4838934 76 Gene 76 0.7509497 1.7891908 0.111590341 0.9913757 -4.8250883 54 Gene 54 -0.5448046 -1.5837617 0.152121135 0.9913757 -5.0988743 16 Gene 16 -0.4970755 -1.7635064 0.116040512 0.9913757 -4.8601096 94 Gene 94 -0.4921761 -2.2254793 0.056881214 0.9913757 -4.2056166 93 Gene 93 -0.4574475 -1.3155906 0.224961686 0.9913757 -5.4296133 87 Gene 87 -0.4030807 -2.1728549 0.061734408 0.9913757 -4.2822231 92 Gene 92 -0.3708210 -1.3967269 0.200232145 0.9913757 -5.3332685 38 Gene 38 -0.3693720 -1.6087183 0.146559311 0.9913757 -5.0664427 41 Gene 41 0.3296712 0.3589144 0.728998939 0.9913757 -6.2080737 51 Gene 51 -0.3236529 -1.0426366 0.327764650 0.9913757 -5.7250738 40 Gene 40 0.3003745 1.3450728 0.215687305 0.9913757 -5.3950122 83 Gene 83 0.2948265 1.5802816 0.152911783 0.9913757 -5.1033766 47 Gene 47 0.2914631 1.4141449 0.195242647 0.9913757 -5.3121375 39 Gene 39 -0.2828721 -1.5449138 0.161161348 0.9913757 -5.1488494 69 Gene 69 0.2757269 1.2502012 0.246750193 0.9913757 -5.5046099 19 Gene 19 0.2684281 1.2757686 0.238027563 0.9913757 -5.4755794 3 Gene 3 -0.2642721 -1.6381776 0.140233597 0.9913757 -5.0278441 > design <- cbind(Grp1=c(1,1,1,0,0,0),Grp2=c(0,0,0,1,1,1)) > cont.matrix <- makeContrasts(G1vsG2=Grp1-Grp2, levels=design) > fit1<- lmFit(y,design) > fit1 <- contrasts.fit(fit1, cont.matrix) > fit1 <- eBayes(fit1) > topTable(fit1,coef=1,n=20,sort.by="logFC") ID logFC t P.Value adj.P.Val B 2 Gene 2 -2.0639600 -7.2886732 8.728373e-05 0.008728373 1.9349063 1 Gene 1 -1.9752877 -5.0131716 1.053468e-03 0.035115593 -0.6626835 33 Gene 33 1.9059513 5.1993066 8.380943e-04 0.035115593 -0.4227207 23 Gene 23 -1.6921631 -2.1549881 6.347344e-02 0.793418043 -4.8748108 93 Gene 93 -1.5367983 -3.1252258 1.421720e-02 0.313735106 -3.3751312 27 Gene 27 1.0978401 3.0600772 1.568676e-02 0.313735106 -3.4759186 54 Gene 54 -0.7961582 -1.6365658 1.405731e-01 0.949255469 -5.6234924 37 Gene 37 -0.6926892 -2.2573983 5.412387e-02 0.773198083 -4.7195118 42 Gene 42 -0.6895975 -0.9536791 3.683231e-01 0.952227637 -6.4283974 76 Gene 76 0.6887321 1.1603291 2.795521e-01 0.952227637 -6.2154077 87 Gene 87 -0.6277337 -2.3927605 4.383747e-02 0.730624439 -4.5120531 51 Gene 51 0.6097573 1.3889781 2.024874e-01 0.952227637 -5.9464611 41 Gene 41 -0.5810686 -0.4473240 6.665707e-01 0.952227637 -6.7979655 15 Gene 15 0.5801261 1.8016224 1.094946e-01 0.898763677 -5.3938078 7 Gene 7 -0.5487280 -0.4418824 6.703406e-01 0.952227637 -6.8005963 80 Gene 80 -0.5459118 -1.5641098 1.566352e-01 0.949255469 -5.7209503 39 Gene 39 -0.5139565 -1.9848398 8.263317e-02 0.875815090 -5.1285443 16 Gene 16 -0.5085620 -1.2758028 2.380161e-01 0.952227637 -6.0835464 95 Gene 95 0.5040813 0.9693600 3.609117e-01 0.952227637 -6.4133578 69 Gene 69 0.4815805 1.5440263 1.613734e-01 0.949255469 -5.7475589 [/code] I want to know why the output are different. Which one should I believe? Thanks a lot. Yours sincerely, Jianhong Ou jianhong.ou at umassmed.edu
ADD COMMENTlink modified 7.7 years ago by James W. MacDonald48k • written 7.7 years ago by Ou, Jianhong1.1k
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gravatar for James W. MacDonald
7.7 years ago by
United States
James W. MacDonald48k wrote:
Hi Jianhong Ou, On 4/6/2011 3:07 PM, Ou, Jianhong wrote: > Hi all, > > In the usersguide.pdf of limma, there are two approach for two groups (8.4, Two Groups: Common Reference) > > At first, I thought they are same, but... > > [code] >> sd<- 0.3*sqrt(4/rchisq(100,df=4)) >> y<- matrix(rnorm(100*6,sd=sd),100,6) >> rownames(y)<- paste("Gene",1:100) >> y[1:2,4:6]<- y[1:2,4:6] + 2 >> design<- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1)) >> fit<- lmFit(y,design) >> fit<- eBayes(fit) >> topTable(fit,coef=1,n=20,sort.by="logFC") You made a mistake here. You want coef = 2, which is the contrast between groups 1 and 2. What your code is doing is testing to see if the average of the first group is equal to zero or not. Best, Jim > ID logFC t P.Value adj.P.Val B > 33 Gene 33 1.2800739 4.9383702 0.001156486 0.1156486 -0.4485569 > 23 Gene 23 -0.9290832 -1.6732940 0.133021448 0.9913757 -4.9814041 > 27 Gene 27 0.8616516 3.3965657 0.009487765 0.4743882 -2.4838934 > 76 Gene 76 0.7509497 1.7891908 0.111590341 0.9913757 -4.8250883 > 54 Gene 54 -0.5448046 -1.5837617 0.152121135 0.9913757 -5.0988743 > 16 Gene 16 -0.4970755 -1.7635064 0.116040512 0.9913757 -4.8601096 > 94 Gene 94 -0.4921761 -2.2254793 0.056881214 0.9913757 -4.2056166 > 93 Gene 93 -0.4574475 -1.3155906 0.224961686 0.9913757 -5.4296133 > 87 Gene 87 -0.4030807 -2.1728549 0.061734408 0.9913757 -4.2822231 > 92 Gene 92 -0.3708210 -1.3967269 0.200232145 0.9913757 -5.3332685 > 38 Gene 38 -0.3693720 -1.6087183 0.146559311 0.9913757 -5.0664427 > 41 Gene 41 0.3296712 0.3589144 0.728998939 0.9913757 -6.2080737 > 51 Gene 51 -0.3236529 -1.0426366 0.327764650 0.9913757 -5.7250738 > 40 Gene 40 0.3003745 1.3450728 0.215687305 0.9913757 -5.3950122 > 83 Gene 83 0.2948265 1.5802816 0.152911783 0.9913757 -5.1033766 > 47 Gene 47 0.2914631 1.4141449 0.195242647 0.9913757 -5.3121375 > 39 Gene 39 -0.2828721 -1.5449138 0.161161348 0.9913757 -5.1488494 > 69 Gene 69 0.2757269 1.2502012 0.246750193 0.9913757 -5.5046099 > 19 Gene 19 0.2684281 1.2757686 0.238027563 0.9913757 -5.4755794 > 3 Gene 3 -0.2642721 -1.6381776 0.140233597 0.9913757 -5.0278441 >> design<- cbind(Grp1=c(1,1,1,0,0,0),Grp2=c(0,0,0,1,1,1)) >> cont.matrix<- makeContrasts(G1vsG2=Grp1-Grp2, levels=design) >> fit1<- lmFit(y,design) >> fit1<- contrasts.fit(fit1, cont.matrix) >> fit1<- eBayes(fit1) >> topTable(fit1,coef=1,n=20,sort.by="logFC") > ID logFC t P.Value adj.P.Val B > 2 Gene 2 -2.0639600 -7.2886732 8.728373e-05 0.008728373 1.9349063 > 1 Gene 1 -1.9752877 -5.0131716 1.053468e-03 0.035115593 -0.6626835 > 33 Gene 33 1.9059513 5.1993066 8.380943e-04 0.035115593 -0.4227207 > 23 Gene 23 -1.6921631 -2.1549881 6.347344e-02 0.793418043 -4.8748108 > 93 Gene 93 -1.5367983 -3.1252258 1.421720e-02 0.313735106 -3.3751312 > 27 Gene 27 1.0978401 3.0600772 1.568676e-02 0.313735106 -3.4759186 > 54 Gene 54 -0.7961582 -1.6365658 1.405731e-01 0.949255469 -5.6234924 > 37 Gene 37 -0.6926892 -2.2573983 5.412387e-02 0.773198083 -4.7195118 > 42 Gene 42 -0.6895975 -0.9536791 3.683231e-01 0.952227637 -6.4283974 > 76 Gene 76 0.6887321 1.1603291 2.795521e-01 0.952227637 -6.2154077 > 87 Gene 87 -0.6277337 -2.3927605 4.383747e-02 0.730624439 -4.5120531 > 51 Gene 51 0.6097573 1.3889781 2.024874e-01 0.952227637 -5.9464611 > 41 Gene 41 -0.5810686 -0.4473240 6.665707e-01 0.952227637 -6.7979655 > 15 Gene 15 0.5801261 1.8016224 1.094946e-01 0.898763677 -5.3938078 > 7 Gene 7 -0.5487280 -0.4418824 6.703406e-01 0.952227637 -6.8005963 > 80 Gene 80 -0.5459118 -1.5641098 1.566352e-01 0.949255469 -5.7209503 > 39 Gene 39 -0.5139565 -1.9848398 8.263317e-02 0.875815090 -5.1285443 > 16 Gene 16 -0.5085620 -1.2758028 2.380161e-01 0.952227637 -6.0835464 > 95 Gene 95 0.5040813 0.9693600 3.609117e-01 0.952227637 -6.4133578 > 69 Gene 69 0.4815805 1.5440263 1.613734e-01 0.949255469 -5.7475589 > [/code] > > I want to know why the output are different. Which one should I believe? > > Thanks a lot. > > Yours sincerely, > > Jianhong Ou > > jianhong.ou at umassmed.edu > > _______________________________________________ > 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
ADD COMMENTlink written 7.7 years ago by James W. MacDonald48k
Hi Jim, Yes, they are exactly same now. Thanks a lots. Yours sincerely, Jianhong Ou jianhong.ou at umassmed.edu On Apr 6, 2011, at 4:57 PM, James W. MacDonald wrote: > Hi Jianhong Ou, > > On 4/6/2011 3:07 PM, Ou, Jianhong wrote: >> Hi all, >> >> In the usersguide.pdf of limma, there are two approach for two groups (8.4, Two Groups: Common Reference) >> >> At first, I thought they are same, but... >> >> [code] >>> sd<- 0.3*sqrt(4/rchisq(100,df=4)) >>> y<- matrix(rnorm(100*6,sd=sd),100,6) >>> rownames(y)<- paste("Gene",1:100) >>> y[1:2,4:6]<- y[1:2,4:6] + 2 >>> design<- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1)) >>> fit<- lmFit(y,design) >>> fit<- eBayes(fit) >>> topTable(fit,coef=1,n=20,sort.by="logFC") > > You made a mistake here. You want coef = 2, which is the contrast > between groups 1 and 2. What your code is doing is testing to see if the > average of the first group is equal to zero or not. > > Best, > > Jim > >> ID logFC t P.Value adj.P.Val B >> 33 Gene 33 1.2800739 4.9383702 0.001156486 0.1156486 -0.4485569 >> 23 Gene 23 -0.9290832 -1.6732940 0.133021448 0.9913757 -4.9814041 >> 27 Gene 27 0.8616516 3.3965657 0.009487765 0.4743882 -2.4838934 >> 76 Gene 76 0.7509497 1.7891908 0.111590341 0.9913757 -4.8250883 >> 54 Gene 54 -0.5448046 -1.5837617 0.152121135 0.9913757 -5.0988743 >> 16 Gene 16 -0.4970755 -1.7635064 0.116040512 0.9913757 -4.8601096 >> 94 Gene 94 -0.4921761 -2.2254793 0.056881214 0.9913757 -4.2056166 >> 93 Gene 93 -0.4574475 -1.3155906 0.224961686 0.9913757 -5.4296133 >> 87 Gene 87 -0.4030807 -2.1728549 0.061734408 0.9913757 -4.2822231 >> 92 Gene 92 -0.3708210 -1.3967269 0.200232145 0.9913757 -5.3332685 >> 38 Gene 38 -0.3693720 -1.6087183 0.146559311 0.9913757 -5.0664427 >> 41 Gene 41 0.3296712 0.3589144 0.728998939 0.9913757 -6.2080737 >> 51 Gene 51 -0.3236529 -1.0426366 0.327764650 0.9913757 -5.7250738 >> 40 Gene 40 0.3003745 1.3450728 0.215687305 0.9913757 -5.3950122 >> 83 Gene 83 0.2948265 1.5802816 0.152911783 0.9913757 -5.1033766 >> 47 Gene 47 0.2914631 1.4141449 0.195242647 0.9913757 -5.3121375 >> 39 Gene 39 -0.2828721 -1.5449138 0.161161348 0.9913757 -5.1488494 >> 69 Gene 69 0.2757269 1.2502012 0.246750193 0.9913757 -5.5046099 >> 19 Gene 19 0.2684281 1.2757686 0.238027563 0.9913757 -5.4755794 >> 3 Gene 3 -0.2642721 -1.6381776 0.140233597 0.9913757 -5.0278441 >>> design<- cbind(Grp1=c(1,1,1,0,0,0),Grp2=c(0,0,0,1,1,1)) >>> cont.matrix<- makeContrasts(G1vsG2=Grp1-Grp2, levels=design) >>> fit1<- lmFit(y,design) >>> fit1<- contrasts.fit(fit1, cont.matrix) >>> fit1<- eBayes(fit1) >>> topTable(fit1,coef=1,n=20,sort.by="logFC") >> ID logFC t P.Value adj.P.Val B >> 2 Gene 2 -2.0639600 -7.2886732 8.728373e-05 0.008728373 1.9349063 >> 1 Gene 1 -1.9752877 -5.0131716 1.053468e-03 0.035115593 -0.6626835 >> 33 Gene 33 1.9059513 5.1993066 8.380943e-04 0.035115593 -0.4227207 >> 23 Gene 23 -1.6921631 -2.1549881 6.347344e-02 0.793418043 -4.8748108 >> 93 Gene 93 -1.5367983 -3.1252258 1.421720e-02 0.313735106 -3.3751312 >> 27 Gene 27 1.0978401 3.0600772 1.568676e-02 0.313735106 -3.4759186 >> 54 Gene 54 -0.7961582 -1.6365658 1.405731e-01 0.949255469 -5.6234924 >> 37 Gene 37 -0.6926892 -2.2573983 5.412387e-02 0.773198083 -4.7195118 >> 42 Gene 42 -0.6895975 -0.9536791 3.683231e-01 0.952227637 -6.4283974 >> 76 Gene 76 0.6887321 1.1603291 2.795521e-01 0.952227637 -6.2154077 >> 87 Gene 87 -0.6277337 -2.3927605 4.383747e-02 0.730624439 -4.5120531 >> 51 Gene 51 0.6097573 1.3889781 2.024874e-01 0.952227637 -5.9464611 >> 41 Gene 41 -0.5810686 -0.4473240 6.665707e-01 0.952227637 -6.7979655 >> 15 Gene 15 0.5801261 1.8016224 1.094946e-01 0.898763677 -5.3938078 >> 7 Gene 7 -0.5487280 -0.4418824 6.703406e-01 0.952227637 -6.8005963 >> 80 Gene 80 -0.5459118 -1.5641098 1.566352e-01 0.949255469 -5.7209503 >> 39 Gene 39 -0.5139565 -1.9848398 8.263317e-02 0.875815090 -5.1285443 >> 16 Gene 16 -0.5085620 -1.2758028 2.380161e-01 0.952227637 -6.0835464 >> 95 Gene 95 0.5040813 0.9693600 3.609117e-01 0.952227637 -6.4133578 >> 69 Gene 69 0.4815805 1.5440263 1.613734e-01 0.949255469 -5.7475589 >> [/code] >> >> I want to know why the output are different. Which one should I believe? >> >> Thanks a lot. >> >> Yours sincerely, >> >> Jianhong Ou >> >> jianhong.ou at umassmed.edu >> >> _______________________________________________ >> 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 > >
ADD REPLYlink written 7.7 years ago by Ou, Jianhong1.1k
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