[BioC] question about design for limma time course, 2 conditions and drug treatment microarray experiment

[Pekka Kohonen]

Deat Ninni,

It seems that Gordon answered your question in " [BioC] Can I input
ordinal variables into a model in Limma?". So the best way to analyze
your data would be to use the dose (and maybe time as well, or do them
seperately) as ordinal variables, just as he describes in his post. Or
alternatively use them as quantitative variables, but using them as
ordinal variables would be maybe more robust and capture also
non-linear trends. It is very cool that you can actually do this in
limma! I will also try this for my data. I wonder if this can also be
done with camera/mroast, which would enable dose response pathway
analysis to be carried out as well.

Best Regards, Pekka

2013/11/22 Ninni Nahm [guest] <guest at bioconductor.org>:
>
> Dear list,
>
> I have a conceptual question about creating a design matrix for a more complicated experimental design.
>
> I have microarray data of
> - two different conditions (treatment/control),
> - over a series of time points (20, 45, 90, 180 minutes)
> - and different dose concentrations of a certain drug (no treatment, 1mg, 2mg, 3mg, 4mg, 5mg).
> - and I have 2 replicates per drug, time point, and condition
> I think, I know how to do it when I want to consider time only (please correct me when I'm wrong!):
>
> ## find genes which change over time differently between the treatment and the control.
>
> cont.dif <- makeContrasts(
> Dif1  = (treatment_1mg_tp45-treatment_1mg_tp20)-(control_tp45-control_tp20),
> Dif2  = (treatment_1mg_tp90-treatment_1mg_tp45)-(control_tp90-control_tp45),
> Dif3  = (treatment_1mg_tp180-treatment_1mg_tp90)-(control_tp180-control_tp90),
> levels=design)
>
> However, I would like to know which genes are changing over time and drug exposure between control and treatment.
> Would I have to do the contrasts for every dose?
>
> Any help is much appreciated!
>
> Best,
> Ninni
>
>
>
>  -- output of sessionInfo():
>
>> sessionInfo()
> R version 3.0.2 (2013-09-25)
> Platform: x86_64-pc-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] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] sva_3.8.0     mgcv_1.7-27   nlme_3.1-113  corpcor_1.6.6 limma_3.18.3
>
> loaded via a namespace (and not attached):
> [1] grid_3.0.2      lattice_0.20-24 Matrix_1.1-0
>>
>
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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