> dir() [1] "G05.CEL" "G09.CEL" "G10.CEL" "G12.CEL" "G15.CEL" [6] "G19.CEL" "GF.CEL" "GM.CEL" "H044.CEL" "H05.CEL" [11] "H07.CEL" "H10.CEL" "H11.CEL" "H14.CEL" "hgu133acdf" [16] "N01.CEL" "N02.CEL" "N03.CEL" > library(limma) > library(affy) Loading required package: Biobase Loading required package: tools Welcome to Bioconductor Vignettes contain introductory material. To view, simply type: openVignette() For details on reading vignettes, see the openVignette help page. Loading required package: reposTools > Data <- ReadAffy() > eset <- rma(Data) Background correcting Normalizing Calculating Expression > pData(eset) sample G05.CEL 1 G09.CEL 2 G10.CEL 3 G12.CEL 4 G15.CEL 5 G19.CEL 6 GF.CEL 7 GM.CEL 8 H044.CEL 9 H05.CEL 10 H07.CEL 11 H10.CEL 12 H11.CEL 13 H14.CEL 14 N01.CEL 15 N02.CEL 16 N03.CEL 17 > tissue <- c("C","C","C","C","C","C","C","C","C","C","C","C","C","C","N","N","N") > design <- model.matrix(~factor(tissue)) > colnames(design) <- c("C", "CvsN") > design C CvsN 1 1 0 2 1 0 3 1 0 4 1 0 5 1 0 6 1 0 7 1 0 8 1 0 9 1 0 10 1 0 11 1 0 12 1 0 13 1 0 14 1 0 15 1 1 16 1 1 17 1 1 attr(,"assign") [1] 0 1 attr(,"contrasts") attr(,"contrasts")$"factor(tissue)" [1] "contr.treatment" > fit <-lmFit(eset,design) > fit <-eBayes(fit) > options(digits=2) > topTable(fit,coef=2,n=50,adjust="fdr") ID M A t P.Value B 22193 78047_s_at 0.60 7.3 5.3 0.82 -3.4 2594 203065_s_at -1.26 6.7 -5.0 0.82 -3.5 10680 211245_x_at 0.58 4.9 4.7 1.00 -3.6 17919 218554_s_at 0.59 4.7 4.5 1.00 -3.6 9431 209945_s_at -0.67 6.1 -4.5 1.00 -3.6 4556 205029_s_at 3.09 3.6 4.4 1.00 -3.6 4557 205030_at 3.58 4.6 4.3 1.00 -3.6 5845 206319_s_at 0.82 4.0 4.3 1.00 -3.7 21838 36019_at 0.67 6.7 4.2 1.00 -3.7 5209 205682_x_at 0.61 4.8 4.2 1.00 -3.7 6791 207266_x_at -0.95 7.8 -4.0 1.00 -3.7 21916 38447_at 0.66 7.3 4.0 1.00 -3.7 21914 38340_at 0.59 6.3 3.9 1.00 -3.8 16241 216871_at 0.59 3.4 3.9 1.00 -3.8 982 201454_s_at -0.65 6.2 -3.9 1.00 -3.8 22024 46256_at 0.62 7.2 3.9 1.00 -3.8 7489 207978_s_at 0.47 4.3 3.8 1.00 -3.8 4452 204925_at 0.48 5.0 3.8 1.00 -3.8 7121 207600_at 0.48 5.5 3.7 1.00 -3.8 12443 213060_s_at 1.41 6.0 3.7 1.00 -3.8 1619 202091_at 0.51 3.3 3.7 1.00 -3.8 9890 210412_at 0.53 3.5 3.6 1.00 -3.8 21922 38707_r_at 0.45 7.8 3.6 1.00 -3.9 2715 203187_at 0.59 5.8 3.6 1.00 -3.9 3354 203827_at -0.99 5.5 -3.6 1.00 -3.9 5340 205813_s_at 0.52 5.8 3.5 1.00 -3.9 2445 202916_s_at -0.61 6.1 -3.5 1.00 -3.9 18810 219446_at -0.68 5.9 -3.5 1.00 -3.9 14010 214632_at -0.54 4.2 -3.4 1.00 -3.9 2915 203388_at 0.46 6.2 3.4 1.00 -3.9 21936 396_f_at 0.70 7.7 3.4 1.00 -3.9 16292 216922_x_at 0.61 3.8 3.4 1.00 -3.9 13378 213999_at 0.44 4.5 3.4 1.00 -3.9 9642 210158_at 0.58 4.4 3.4 1.00 -3.9 19117 219753_at 0.65 5.6 3.4 1.00 -3.9 10820 211405_x_at 0.53 5.3 3.4 1.00 -3.9 19242 219878_s_at -0.58 4.5 -3.4 1.00 -3.9 3275 203748_x_at -0.90 7.9 -3.4 1.00 -3.9 16554 217187_at 0.58 5.7 3.4 1.00 -3.9 8627 209133_s_at 0.54 4.7 3.3 1.00 -3.9 17983 218618_s_at -1.15 8.0 -3.3 1.00 -3.9 20977 221615_at 0.50 3.7 3.3 1.00 -3.9 18562 219198_at 0.54 5.7 3.3 1.00 -3.9 19513 220149_at 0.58 4.8 3.3 1.00 -3.9 1770 202242_at 1.04 5.4 3.3 1.00 -3.9 10081 210616_s_at -0.56 8.4 -3.3 1.00 -3.9 17995 218630_at 0.37 5.4 3.3 1.00 -3.9 3018 203491_s_at -0.67 5.1 -3.3 1.00 -3.9 10823 211410_x_at 0.56 5.3 3.3 1.00 -3.9 16351 216981_x_at 0.57 6.3 3.3 1.00 -3.9 >