Hi, I'm confused about creating a contrast matrix with Limma. I've got three groups (LA1777_X, LA_1777_6, LA1777_LVS), each replicted threefold. I would like to use the group LA1777_LVS as a "common reference" to find genes that are expressed differently between LA1777_X and LA1777_6. The design looks like this: LA1777_LVS LA1777_6 LA1777_X 1 0 0 1 2 0 0 1 3 0 0 1 4 0 1 0 5 0 1 0 6 0 1 0 7 1 0 0 8 1 0 0 9 1 0 0 attr(,"assign") [1] 1 1 1 attr(,"contrasts") attr(,"contrasts")$target [1] "contr.treatment" My contrast.matrix looks like this contrast <- makeContrasts(diff1 = (LA1777_6-LA1777_LVS) - (LA1777_X- LA1777_LVS), diff2 = LA1777_6-LA1777_X, levels = design) > contrast Contrasts Levels diff1 diff2 LA1777_LVS 0 0 LA1777_6 1 1 LA1777_X -1 -1 I would expect from the contrast diff1 to find genes that differing between LA1777_6/LA1777_LVS and LA1777_X/LA1777_LVS, but diff2 give the same contrast and result. Can you show me how to set up the right design and contrast matrix? Best regards, Benedikt -- output of sessionInfo(): R version 3.0.0 Patched (2013-04-04 r62494) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] topGO_2.14.0 SparseM_1.03 GO.db_2.10.1 RSQLite_0.11.4 DBI_0.2-7 AnnotationDbi_1.24.0 graph_1.40.0 gtools_3.3.0 [9] MapManITAG2.3_1.0 biomaRt_2.18.0 RColorBrewer_1.0-5 gplots_2.12.1 hopach_2.22.0 cluster_1.14.4 limma_3.18.13 Biobase_2.22.0 [17] BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] IRanges_1.20.6 KernSmooth_2.23-10 RCurl_1.95-4.1 XML_3.98-1.1 bitops_1.0-6 caTools_1.16 gdata_2.13.2 grid_3.0.0 lattice_0.20-24 [10] stats4_3.0.0 tools_3.0.0 -- Sent via the guest posting facility at bioconductor.org.

Hi Benedikt, On 7/25/2014 7:41 AM, Benedikt Athmer [guest] wrote: > Hi, > > I'm confused about creating a contrast matrix with Limma. I've got three groups (LA1777_X, LA_1777_6, LA1777_LVS), each replicted threefold. I would like to use the group LA1777_LVS as a "common reference" to find genes that are expressed differently between LA1777_X and LA1777_6. > > The design looks like this: > > LA1777_LVS LA1777_6 LA1777_X > 1 0 0 1 > 2 0 0 1 > 3 0 0 1 > 4 0 1 0 > 5 0 1 0 > 6 0 1 0 > 7 1 0 0 > 8 1 0 0 > 9 1 0 0 > attr(,"assign") > [1] 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$target > [1] "contr.treatment" > > My contrast.matrix looks like this > > contrast <- makeContrasts(diff1 = (LA1777_6-LA1777_LVS) - (LA1777_X- LA1777_LVS), > diff2 = LA1777_6-LA1777_X, levels = design) >> contrast > Contrasts > Levels diff1 diff2 > LA1777_LVS 0 0 > LA1777_6 1 1 > LA1777_X -1 -1 The two contrasts you have set up are identical, which is why you get identical results. In other words, if you take (x - y) - (z - y) this is the same as x - y - z + y which simplifies to x - z The term 'common reference' is in general used when analyzing two- color arrays, where the Cy3 channel is an aliquot of the same reference cDNA. In that situation the common reference can be used to help eliminate some technical variability, as the input is identical, so any differences in fluorescent intensities can be assumed to be technical in nature. But in your case you cannot gain anything by using a different array as a common reference. Best, Jim > > I would expect from the contrast diff1 to find genes that differing between LA1777_6/LA1777_LVS and LA1777_X/LA1777_LVS, but diff2 give the same contrast and result. > Can you show me how to set up the right design and contrast matrix? > > Best regards, Benedikt > > > -- output of sessionInfo(): > > R version 3.0.0 Patched (2013-04-04 r62494) > Platform: x86_64-unknown-linux-gnu (64-bit) > > locale: > [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C LC_PAPER=C LC_NAME=C > [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C > > attached base packages: > [1] parallel stats graphics grDevices utils datasets methods base > > other attached packages: > [1] topGO_2.14.0 SparseM_1.03 GO.db_2.10.1 RSQLite_0.11.4 DBI_0.2-7 AnnotationDbi_1.24.0 graph_1.40.0 gtools_3.3.0 > [9] MapManITAG2.3_1.0 biomaRt_2.18.0 RColorBrewer_1.0-5 gplots_2.12.1 hopach_2.22.0 cluster_1.14.4 limma_3.18.13 Biobase_2.22.0 > [17] BiocGenerics_0.8.0 > > loaded via a namespace (and not attached): > [1] IRanges_1.20.6 KernSmooth_2.23-10 RCurl_1.95-4.1 XML_3.98-1.1 bitops_1.0-6 caTools_1.16 gdata_2.13.2 grid_3.0.0 lattice_0.20-24 > [10] stats4_3.0.0 tools_3.0.0 > > -- > 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 > -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099