Hi, I have a experiment design that mirrors what is written in the edgeR vignette in section 3.5 Comparisons Both Between and Within Subjects. I have two groups of cell lines: Resistant to a drug and sensitive to a drug. For each cell line in these groups, I have a control, a treatment after 2h and treatment after 24h. Following the vignette, I have renumbered the cell lines within each group and set up the nested design: design = model.matrix(~resist+resist:sub+resist:treat) design (Intercept) resistY resistN:sub2 resistY:sub2 resistN:sub3 resistY:sub3 1 1 0 0 0 0 0 2 1 0 0 0 0 0 3 1 0 0 0 0 0 4 1 0 1 0 0 0 5 1 0 1 0 0 0 6 1 0 1 0 0 0 7 1 0 0 0 1 0 8 1 0 0 0 1 0 9 1 0 0 0 1 0 10 1 1 0 0 0 0 11 1 1 0 0 0 0 12 1 1 0 0 0 0 13 1 1 0 1 0 0 14 1 1 0 1 0 0 15 1 1 0 1 0 0 16 1 1 0 0 0 1 17 1 1 0 0 0 1 18 1 1 0 0 0 1 resistN:treat24h resistY:treat24h resistN:treat2h resistY:treat2h 1 0 0 0 0 2 0 0 1 0 3 1 0 0 0 4 0 0 0 0 5 0 0 1 0 6 1 0 0 0 7 0 0 0 0 8 0 0 1 0 9 1 0 0 0 10 0 0 0 0 11 0 0 0 1 12 0 1 0 0 13 0 0 0 0 14 0 0 0 1 15 0 1 0 0 16 0 0 0 0 17 0 0 0 1 18 0 1 0 0 I can now calculate differentially expressed genes between resistant and sensitive cells in the 2h treatment easily: glmLRT(fit,contrast=c(0,0,0,0,0,0,0,0,-1,1)) Same goes for the same question in the 24h treatment. glmLRT(fit,contrast=c(0,0,0,0,0,0,-1,1,0,0)) >From the vignette and design matrix it is however unclear to me how to formulate contrasts for e.g. the question on differentially expressed genes between sensitive/resistant control cells. The contrast c(0,1,0,0,0,0,0,0,0,0) would just give me differentially expressed genes between sensitive/resistant cells in any condition judging from the matrix. So how would I test this? It's also unclear to me how to test for more complex questions e.g. differentially expressed genes between sensitive/resistant cells in the 24h treatment that are not differentially expressed between sensitive/resistant cells in the control. Would I simply have to combine the results from the 24h treatment test and remove any genes that also pop up in the control test? Thanks for your help! -- output of sessionInfo(): sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] splines parallel stats graphics grDevices utils datasets [8] methods base other attached packages: [1] genefilter_1.44.0 RColorBrewer_1.0-5 geneplotter_1.40.0 [4] annotate_1.40.1 AnnotationDbi_1.24.0 lattice_0.20-29 [7] Biobase_2.22.0 gplots_2.13.0 ggplot2_0.9.3.1 [10] edgeR_3.4.2 limma_3.18.13 DESeq2_1.2.10 [13] RcppArmadillo_0.4.200.0 Rcpp_0.11.1 GenomicRanges_1.14.4 [16] XVector_0.2.0 IRanges_1.20.7 BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] bitops_1.0-6 caTools_1.16 colorspace_1.2-4 DBI_0.2-7 [5] dichromat_2.0-0 digest_0.6.4 gdata_2.13.3 grid_3.0.2 [9] gtable_0.1.2 gtools_3.3.1 KernSmooth_2.23-12 labeling_0.2 [13] locfit_1.5-9.1 MASS_7.3-31 munsell_0.4.2 plyr_1.8.1 [17] proto_0.3-10 reshape2_1.2.2 RSQLite_0.11.4 scales_0.2.3 [21] stats4_3.0.2 stringr_0.6.2 survival_2.37-7 tools_3.0.2 [25] XML_3.98-1.1 xtable_1.7-3 -- Sent via the guest posting facility at bioconductor.org.