Question: DESeq analysis of resistance data
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4.9 years ago by
Guest User12k
Guest User12k 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. 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.
rnaseq cancer deseq2 • 501 views
ADD COMMENTlink modified 4.9 years ago by Michael Love23k • written 4.9 years ago by Guest User12k
Answer: DESeq analysis of resistance data
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4.9 years ago by
Michael Love23k
United States
Michael Love23k 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
ADD COMMENTlink written 4.9 years ago by Michael Love23k
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@gmail.com> wrote: > hi Dave, > > On Sun, Jun 15, 2014 at 8:46 AM, Dave Wettmann [guest] > <guest@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@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
ADD REPLYlink written 4.9 years ago by Dave Wettmann40
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 > >
ADD REPLYlink written 4.9 years ago by Michael Love23k
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