R version 3.0.3 (2014-03-06) Platform: i386-w64-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 LC_MONETARY=German_Germany.1252 [4] LC_NUMERIC=C LC_TIME=German_Germany.1252 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq2_1.2.10 RcppArmadillo_0.4.100.2.1 Rcpp_0.11.1 GenomicRanges_1.14.4 [5] XVector_0.2.0 IRanges_1.20.7 BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] annotate_1.40.1 AnnotationDbi_1.24.0 Biobase_2.22.0 DBI_0.2-7 [5] genefilter_1.44.0 grid_3.0.3 lattice_0.20-29 locfit_1.5-9.1 [9] RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.0.3 stats4_3.0.3 [13] survival_2.37-7 tools_3.0.3 XML_3.98-1.1 xtable_1.7-3 > > se=data > head(se) Bur_1_1 Bur_1_2 Bur_1_3 Bur_2_1 Bur_2_2 Bur_2_3 Col_1_1 Col_1_2 Col_1_3 Col_2_1 Col_2_2 Col_2_3 AT1G01010 97 141 117 60 51 125 51 32 87 19 12 42 AT1G01020 144 149 125 152 128 96 198 167 158 88 119 130 AT1G01030 65 28 51 39 25 29 33 27 68 21 16 47 AT1G01040 788 843 677 655 530 548 791 419 533 369 302 419 AT1G01046 8 11 8 7 3 7 9 3 6 6 1 3 AT1G01050 663 573 545 556 548 649 1015 818 781 746 719 793 pool01 pool02 pool03 pool04 pool05 pool06 pool07 pool08 pool09 pool10 pool11 pool12 AT1G01010 58 38 82 60 91 51 82 62 72 37 36 87 AT1G01020 137 96 145 120 157 155 118 139 100 141 283 125 AT1G01030 53 30 53 40 64 19 43 15 21 14 11 17 AT1G01040 701 432 592 572 773 454 524 523 534 266 157 330 AT1G01046 11 5 12 7 9 4 5 9 6 2 5 6 AT1G01050 887 729 763 660 764 691 742 790 802 562 507 474 > ExpDesign2 <- data.frame(row.names=colnames(se), genotype=as.factor(c("Bur", "Bur", "Bur", + "Bur", "Bur", "Bur", "Col", "Col", "Col", "Col", "Col", "Col", + "earlyflowering", "earlyflowering", "earlyflowering", "lateflowering", "lateflowering", + "lateflowering", "earlyflowering", "earlyflowering", "earlyflowering", "lateflowering", + "lateflowering", "lateflowering")), sampling = as.factor(c("tp1", "tp1", "tp1", "tp2", "tp2", "tp2", "tp1", "tp1", "tp1", + "tp2", "tp2", "tp2", "tp1", "tp1", "tp1", "tp1", "tp1", "tp1", "tp2", "tp2", "tp2", "tp2", "tp2", + "tp2"))) > ExpDesign2 genotype sampling Bur_1_1 Bur tp1 Bur_1_2 Bur tp1 Bur_1_3 Bur tp1 Bur_2_1 Bur tp2 Bur_2_2 Bur tp2 Bur_2_3 Bur tp2 Col_1_1 Col tp1 Col_1_2 Col tp1 Col_1_3 Col tp1 Col_2_1 Col tp2 Col_2_2 Col tp2 Col_2_3 Col tp2 pool01 earlyflowering tp1 pool02 earlyflowering tp1 pool03 earlyflowering tp1 pool04 lateflowering tp1 pool05 lateflowering tp1 pool06 lateflowering tp1 pool07 earlyflowering tp2 pool08 earlyflowering tp2 pool09 earlyflowering tp2 pool10 lateflowering tp2 pool11 lateflowering tp2 pool12 lateflowering tp2 > > se_input_nested <- DESeqDataSetFromMatrix(countData = se, colData=ExpDesign2, design=~genotype%in%sampling) Usage note: the following factors have 3 or more levels: genotype For DESeq2 versions < 1.3, if you plan on extracting results for these factors, we recommend using betaPrior=FALSE as an argument when calling DESeq(). As currently implemented in version 1.2, the log2 fold changes can vary if the base level is changed, when extracting results for a factor with 3 or more levels. A solution will be implemented in version 1.3 which allows for the use of a beta prior and symmetric log2 fold change estimates regardless of the selection of base level. Fehler in validObject(.Object) : invalid class “DESeqDataSet” object: the model matrix is not full rank, i.e. one or more variables in the design formula are linear combinations of the others > > ExpDesign <- data.frame(row.names=colnames(se), genotype = as.factor(c("parent", "parent", "parent", + "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", + "pool_lines", "pool_lines", "pool_lines", "pool_lines", "pool_lines", + "pool_lines", "pool_lines", "pool_lines", "pool_lines", "pool_lines", + "pool_lines", "pool_lines")), flowering = as.factor(c("late", "late", "late", + "late", "late", "late", "early", "early", "early", "early", "early", "early", + "early", "early", "early", "late", "late", + "late", "early", "early", "early", "late", + "late", "late")), sampling = as.factor(c("tp1", "tp1", "tp1", "tp2", "tp2", "tp2", "tp1", "tp1", "tp1", + "tp2", "tp2", "tp2", "tp1", "tp1", "tp1", "tp1", "tp1", "tp1", "tp2", "tp2", "tp2", "tp2", "tp2", + "tp2"))) > ExpDesign genotype flowering sampling Bur_1_1 parent late tp1 Bur_1_2 parent late tp1 Bur_1_3 parent late tp1 Bur_2_1 parent late tp2 Bur_2_2 parent late tp2 Bur_2_3 parent late tp2 Col_1_1 parent early tp1 Col_1_2 parent early tp1 Col_1_3 parent early tp1 Col_2_1 parent early tp2 Col_2_2 parent early tp2 Col_2_3 parent early tp2 pool01 pool_lines early tp1 pool02 pool_lines early tp1 pool03 pool_lines early tp1 pool04 pool_lines late tp1 pool05 pool_lines late tp1 pool06 pool_lines late tp1 pool07 pool_lines early tp2 pool08 pool_lines early tp2 pool09 pool_lines early tp2 pool10 pool_lines late tp2 pool11 pool_lines late tp2 pool12 pool_lines late tp2 > se_input_nested <- DESeqDataSetFromMatrix(countData = se, colData=ExpDesign, design=~genotype%in%sampling) Fehler in validObject(.Object) : invalid class “DESeqDataSet” object: the model matrix is not full rank, i.e. one or more variables in the design formula are linear combinations of the others >