[BioC] What's the difference of the two approach for two groups in limma?

[Ou, Jianhong]

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