[BioC] DESeq analysis of resistance data

Michael Love michaelisaiahlove at gmail.com
Sun Jun 15 17:19:27 CEST 2014


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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