[BioC] DESeq analysis of resistance data
Michael Love
michaelisaiahlove at gmail.com
Mon Jun 16 16:00:42 CEST 2014
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
>
>
More information about the Bioconductor
mailing list