hi Dave, You could build the following lists of genes: alpha <- 0.1 resistRes <- results(dds, contrast=c("condition","resistant","sensitive")) resistDE <- rownames(resistRes)[which(resistRes$padj < alpha)] sensRes <- results(dds, contrast=c("condition","sensitive","control")) sensDE <- rownames(sensRes)[which(sensRes$padj < alpha)] # those genes where resistant was different than sensitive, # and sensitive different than control intersect(resistDE, sensDE) # those genes where resistant was different then sensitive, # removing those where sensitive was significantly different than control setdiff(resistDE, sensDE) And remember you can supply these gene lists as indices to the results tables for subsetting: resistRes[ setdiff(resistDE, sensDE), ] On Sun, Jun 15, 2014 at 2:24 PM, Dave Wettmann <david.wettmann at="" gmail.com=""> wrote: > Hi Mike, > > Thanks for your reply; my interest would be in the genes that are > differentially expressed in the resistant cells versus the sensitive cells > but also using the control samples to identify any differentially expressed > genes which changing as a result of a "non-specific" effect of treatment > with the drug. > > Best, > Dave > > > On 15 June 2014 16:19, Michael Love <michaelisaiahlove at="" gmail.com=""> wrote: >> >> hi Dave, >> >> On Sun, Jun 15, 2014 at 8:46 AM, Dave Wettmann [guest] >> <guest at="" bioconductor.org=""> wrote: >> > Hello, >> > >> > I am analysing RNASeq data from cancer cell lines. I have 3 groups with >> > n=5 in each group. One group is sensitive to a drug, the second group has >> > been selected for clones which have become resistant to the drug. The third >> > group is a control, vehicle-treated group. I have used DESeq2 before to >> > compare two groups but I'd be interested in advice on how to analyse these >> > data please. I am interested in identifying differential changes in the >> > resistant group which might be leading to the acquired resistance. >> >> Maybe you can say more about what specific evidence of differential >> expression you are looking for. It sounds like you might have >> something in mind more than those genes which are differently >> expressed in the resistant group compared to the sensitive group. >> >> Note you can contrast any pair of the three levels using the contrast >> argument. See section 3.2 of the vignette. >> >> Mike >> >> > Would analysing these data using an ANOVA model be appropriate? >> > >> > Thanks, >> > Dave >> > >> > -- output of sessionInfo(): >> > >> > R version 3.1.0 (2014-04-10) >> > 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] parallel stats graphics grDevices utils datasets methods >> > [8] base >> > >> > other attached packages: >> > [1] DESeq2_1.4.5 RcppArmadillo_0.4.300.0 Rcpp_0.11.1 >> > [4] GenomicRanges_1.16.3 GenomeInfoDb_1.0.2 IRanges_1.22.7 >> > [7] BiocGenerics_0.10.0 >> > >> > loaded via a namespace (and not attached): >> > [1] annotate_1.42.0 AnnotationDbi_1.26.0 Biobase_2.24.0 >> > [4] DBI_0.2-7 genefilter_1.46.1 geneplotter_1.42.0 >> > [7] grid_3.1.0 lattice_0.20-29 locfit_1.5-9.1 >> > [10] RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.1.0 >> > [13] stats4_3.1.0 survival_2.37-7 XML_3.98-1.1 >> > [16] xtable_1.7-3 XVector_0.4.0 >> > >> > >> > -- >> > 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 > >