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 > >