I have a 12 samples profiled by U133. They are split into 3 groups, WT.U (Wild-type not transfected), WT.P (Wild-type transfected with plasmid only), WT.G (Wild-type transfected with plasmid expressing gene G). The primary aim is to identify differentially expressed genes affected by gene G. The commented script and partial output is given below. The venn diagram in pdf format is attached to this email. My questions are which part of the venn diagram lists only differentially expressed genes affected by gene G ? The output of topTable shows only a single column of F, P.Value, adj.P.Val, what do they mean ? At which step of limma can I extract a list of differentially expressed genes affected by gene G ? #WT.U: Wild-type not transfected #WT.P: Wild-type transfected with plasmid, this is negative control #WT.G: Wild-type transfected with plasmid expressing gene R>m.design <- rep(c("WT.U", "WT.P", "WT.G"), each=4) # sample.dchip.data.xls contains normalized values of U133plus2.0 using dchip R>m <- read.delim("sample.dchip.data.xls", sep="\t", row.names=1, as.is=T) R>colnames(m) <- m.design R>head(m) WT.U WT.U.1 WT.U.2 WT.U.3 WT.P WT.P.1 WT.P.2 WT.P.3 WT.G WT.G.1 WT.G.2 WT.G.3 1007_s_at 10.349609 10.357068 10.471667 10.296924 10.345104 10.314221 10.318749 10.382413 10.359920 10.389718 10.278656 10.251707 1053_at 7.608040 7.634796 7.690094 7.663549 8.258301 8.092113 8.160973 8.068777 8.223478 8.139658 8.087436 8.119274 117_at 6.251763 6.220520 6.340353 6.330107 6.460388 6.287633 6.276603 6.303828 6.315318 6.207221 6.339102 6.398554 121_at 9.515605 9.021253 9.093810 9.170541 9.595528 9.521937 9.662861 9.613140 9.126300 9.240543 9.019469 9.194492 1255_g_at 7.331892 7.015669 7.142806 7.251481 7.208346 7.206259 7.001182 7.281676 6.653735 6.643784 6.621457 6.751383 1294_at 7.761945 7.717853 7.688467 7.773988 7.688812 7.572314 7.730915 7.807970 7.597436 7.602394 7.590732 7.841878 R>design <- model.matrix(~0+factor(m.design, levels=unique(m.design))) R>colnames(design) <- unique(m.design) R>fit <- lmFit(m, design) R>cont.matrix <- makeContrasts("WT.G vs WT.U" =WT.G - WT.U, "WT.G vs WT.P"=WT.G - WT.P, "WT.P vs WT.U"=(WT.G - WT.P) - (WT.G - WT.U), levels=unique(m.design)) R>fit2 <- contrasts.fit(fit, cont.matrix) R>fit2 <- eBayes(fit2) R>results <- decideTests(fit2) R>vennDiagram(results, include=c("up", "down"), counts.col=c("red","green")) R>topTableF(fit2, adjust="BH") ID WT.G.vs.WT.U WT.G.vs.WT.P WT.P.vs.WT.U AveExpr F P.Value adj.P.Val 31508 222227_at -2.4414979 2.6066354 5.04813333 7.145136 2245.9382 5.468295e-20 2.989790e-15 15754 206307_s_at 2.5757787 2.3607819 -0.21499676 7.015439 617.1450 1.263117e-15 3.453047e-11 18657 209242_at -2.2682707 -0.0471402 2.22113050 6.006579 539.2405 3.583991e-15 6.531823e-11 48308 239058_at 1.3057067 1.6484192 0.34271258 6.586163 393.9731 4.020488e-14 5.495505e-10 11885 202436_s_at 1.5394701 1.4423196 -0.09715044 7.286102 323.2023 1.834015e-13 2.005495e-09 40921 231666_at -1.5600316 -1.7072720 -0.14724040 8.503539 307.0483 2.713968e-13 2.473103e-09 10244 200795_at 1.0378595 0.9476124 -0.09024712 6.258110 291.3060 4.055878e-13 2.782933e-09 33848 224588_at 0.9143286 -0.4761739 -1.39050244 11.188861 290.6110 4.130463e-13 2.782933e-09 32483 223204_at 1.4177460 0.9935906 -0.42415536 6.308256 286.6930 4.580960e-13 2.782933e-09 13807 204359_at 1.3443594 0.7638838 -0.58047559 8.089051 276.7941 5.987744e-13 3.273799e-09 -------------- next part -------------- A non-text attachment was scrubbed... Name: vennDiagram.pdf Type: application/pdf Size: 20018 bytes Desc: not available URL: <https: stat.ethz.ch="" pipermail="" bioconductor="" attachments="" 20090305="" 69cadd3d="" attachment.pdf="">