[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.
>> >
>> > _______________________________________________
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>



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