Dear list, I have a conceptual question about creating a design matrix for a more complicated experimental design. I have microarray data of - two different conditions (treatment/control), - over a series of time points (20, 45, 90, 180 minutes) - and different dose concentrations of a certain drug (no treatment, 1mg, 2mg, 3mg, 4mg, 5mg). - and I have 2 replicates per drug, time point, and condition I think, I know how to do it when I want to consider time only (please correct me when I'm wrong!): ## find genes which change over time differently between the treatment and the control. cont.dif <- makeContrasts( Dif1 = (treatment_1mg_tp45-treatment_1mg_tp20)-(control_tp45-control_tp20), Dif2 = (treatment_1mg_tp90-treatment_1mg_tp45)-(control_tp90-control_tp45), Dif3 = (treatment_1mg_tp180-treatment_1mg_tp90)-(control_tp180-control_tp90), levels=design) However, I would like to know which genes are changing over time and drug exposure between control and treatment. Would I have to do the contrasts for every dose? Any help is much appreciated! Best, Ninni -- output of sessionInfo(): > sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-pc-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] stats graphics grDevices utils datasets methods base other attached packages: [1] sva_3.8.0 mgcv_1.7-27 nlme_3.1-113 corpcor_1.6.6 limma_3.18.3 loaded via a namespace (and not attached): [1] grid_3.0.2 lattice_0.20-24 Matrix_1.1-0 > -- Sent via the guest posting facility at bioconductor.org.

Dear Ninni, I have used limma/bioconductor for dose-response analysis but am not going to answer your limma question directly (others can probably do better than I). There is, however, another package called "IsoGeneGUI" for doing dose-response analysis in a model-based manner. It is using a CRAN package called Isogene. I have been reading a book on dose-response analysis that describes the methodology in detail (though the vignette gives enough information to do the analysis). Essentially, model based dose-response analysis is a different paradigm and, it seems to me, cannot be carried out using limma. Since Isogene uses SAM statistics it is not as powerful / sensitive as limma which uses variance shrinkage and parametric statistics (instead of permutations). But it should be very reliable and reasonably sensitive (though SAM-like statistics would especially benefit from having at least 3 biological replicates). I think that model-based dose response analysis makes sense but is a bit of an undeveloped area, possibly because academic investigators have not had so many doses to play around with in the past (and you seem to have quite a few dose in your data). This is now changing when omics profiling is becoming cheaper and there is a debate raging over monotonic vs. non-monotonic dose responses in relation to the hormone mimicking chemicals in the environment, for instance. The authors of IsogeneGUI include people in the pharmaceutical industry also. Best Regards, Pekka 2013/11/22 Ninni Nahm [guest] <guest at="" bioconductor.org="">: > > Dear list, > > I have a conceptual question about creating a design matrix for a more complicated experimental design. > > I have microarray data of > - two different conditions (treatment/control), > - over a series of time points (20, 45, 90, 180 minutes) > - and different dose concentrations of a certain drug (no treatment, 1mg, 2mg, 3mg, 4mg, 5mg). > - and I have 2 replicates per drug, time point, and condition > I think, I know how to do it when I want to consider time only (please correct me when I'm wrong!): > > ## find genes which change over time differently between the treatment and the control. > > cont.dif <- makeContrasts( > Dif1 = (treatment_1mg_tp45-treatment_1mg_tp20)-(control_tp45-control_tp20), > Dif2 = (treatment_1mg_tp90-treatment_1mg_tp45)-(control_tp90-control_tp45), > Dif3 = (treatment_1mg_tp180-treatment_1mg_tp90)-(control_tp180-control_tp90), > levels=design) > > However, I would like to know which genes are changing over time and drug exposure between control and treatment. > Would I have to do the contrasts for every dose? > > Any help is much appreciated! > > Best, > Ninni > > > > -- output of sessionInfo(): > >> sessionInfo() > R version 3.0.2 (2013-09-25) > Platform: x86_64-pc-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] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] sva_3.8.0 mgcv_1.7-27 nlme_3.1-113 corpcor_1.6.6 limma_3.18.3 > > loaded via a namespace (and not attached): > [1] grid_3.0.2 lattice_0.20-24 Matrix_1.1-0 >> > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

Deat Ninni, It seems that Gordon answered your question in " [BioC] Can I input ordinal variables into a model in Limma?". So the best way to analyze your data would be to use the dose (and maybe time as well, or do them seperately) as ordinal variables, just as he describes in his post. Or alternatively use them as quantitative variables, but using them as ordinal variables would be maybe more robust and capture also non-linear trends. It is very cool that you can actually do this in limma! I will also try this for my data. I wonder if this can also be done with camera/mroast, which would enable dose response pathway analysis to be carried out as well. Best Regards, Pekka 2013/11/22 Ninni Nahm [guest] <guest at="" bioconductor.org="">: > > Dear list, > > I have a conceptual question about creating a design matrix for a more complicated experimental design. > > I have microarray data of > - two different conditions (treatment/control), > - over a series of time points (20, 45, 90, 180 minutes) > - and different dose concentrations of a certain drug (no treatment, 1mg, 2mg, 3mg, 4mg, 5mg). > - and I have 2 replicates per drug, time point, and condition > I think, I know how to do it when I want to consider time only (please correct me when I'm wrong!): > > ## find genes which change over time differently between the treatment and the control. > > cont.dif <- makeContrasts( > Dif1 = (treatment_1mg_tp45-treatment_1mg_tp20)-(control_tp45-control_tp20), > Dif2 = (treatment_1mg_tp90-treatment_1mg_tp45)-(control_tp90-control_tp45), > Dif3 = (treatment_1mg_tp180-treatment_1mg_tp90)-(control_tp180-control_tp90), > levels=design) > > However, I would like to know which genes are changing over time and drug exposure between control and treatment. > Would I have to do the contrasts for every dose? > > Any help is much appreciated! > > Best, > Ninni > > > > -- output of sessionInfo(): > >> sessionInfo() > R version 3.0.2 (2013-09-25) > Platform: x86_64-pc-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] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] sva_3.8.0 mgcv_1.7-27 nlme_3.1-113 corpcor_1.6.6 limma_3.18.3 > > loaded via a namespace (and not attached): > [1] grid_3.0.2 lattice_0.20-24 Matrix_1.1-0 >> > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor