Steven | 6 Mar 11:00 2013
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Re: limma edger setting up linear model

Just adding session info in case it's needed.

R version 2.15.2 (2012-10-26)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
LC_MONETARY=en_US.UTF-8
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=C
LC_NAME=C                  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] hthgu133pluspmcdf_2.11.0   genefilter_1.40.0
hthgu133pluspm.db_15.1.0   org.Hs.eg.db_2.8.0
RSQLite_0.11.2
 [6] DBI_0.2-5                  AnnotationDbi_1.20.3
gdata_2.12.0               vsn_3.26.0
affy_1.36.1
[11] arrayQualityMetrics_3.14.0 limma_3.14.4
Biobase_2.18.0             BiocGenerics_0.4.0

loaded via a namespace (and not attached):
 [1] affyio_1.26.0         affyPLM_1.34.0        annotate_1.36.0
beadarray_2.8.1       BeadDataPackR_1.10.0  BiocInstaller_1.8.3
Biostrings_2.26.3
 [8] Cairo_1.5-2           cluster_1.14.3        colorspace_1.2-1
gcrma_2.30.0          grid_2.15.2           gtools_2.7.0
Hmisc_3.10-1
[15] hwriter_1.3           IRanges_1.16.6        KernSmooth_2.23-8
lattice_0.20-13       latticeExtra_0.6-24   parallel_2.15.2
plyr_1.8
[22] preprocessCore_1.20.0 RColorBrewer_1.0-5    reshape2_1.2.2
setRNG_2011.11-2      splines_2.15.2        stats4_2.15.2
stringr_0.6.2
[29] survival_2.37-2       SVGAnnotation_0.93-1  tools_2.15.2
XML_3.95-0.1          xtable_1.7-1          zlibbioc_1.4.0

Kind regards
Steven

--
ir. Steven Wink, PhD student
Division of Toxicology
Leiden/Amsterdam Center for Drug Research (LACDR)
Leiden University
phone: 31-71-5276039

2013/3/5 steven wink <hardervidertsie@...>

> Dear list,
>
> I could not find a fitting example in in the userguides for limma / edger -
> this is probably because of my lack of understanding of multiv. statistics.
>
> I have performed an experiment as follows:
>
>      cell_line treatment time
>          1         1    1
>          1         2    1
>          1         3    1
>          1         4    1
>          1         5    1
>          1         1    2
>          1         2    2
>          1         3    2
>          1         4    2
>          1         5    2
>          2         1    1
>          2         2    1
>          2         3    1
>          2         4    1
>          2         5    1
>          2         1    2
>          2         2    2
>          2         3    2
>          2         4    2
>          2         5    2
>          3         1    1
>          3         2    1
>          3         3    1
>          3         4    1
>          3         5    1
>          3         1    2
>          3         2    2
>          3         3    2
>          3         4    2
>          3         5    2
>
> biological info on the experiment:
> 4 replicates for controls (treatment 1)
> 3 replicates for the other 4 treatments
> the cell lines are actually very similar - stable knock down /
> overexpression versions of each other - so maybe treat as random sample
> when interested in treatment effects?
> The treatments include a negative control, I am also interested in
> different treatment comparisons ( 3 vs 4, 2 vs 5 etc etc) though.
> The effect of time is not really of interest to me, so if it makes it
> easier it would be ok to split the data in 2 sets, 1 for each time point.
>
> biol questions:
> baseline differences in cell iines.
> differences in cell lines response to treatments
> the treatment effects relative to control and to each other.
> Above questions for both time points.
>
> This seems to me to be a factorial design, so first thing I tried was a 3
> factorial design, with a design matrix with all possible combinations:
>
>  >cellLine <- eSetrmaF$cell_line
>     >   treatment <- eSetrmaF$treatment
>        >      time <- eSetrmaF$time
>           >      allCombos <- paste( cellLine, treatment, time, sep = "."
>  )
>              >    allCombos <- factor( allCombos )
>                 >       design <- model.matrix( ~0 + allCombos )
>               >   colnames( design ) <- levels( allCombos )
>              >  fitAll <- lmFit( eSetrmaF, design )
>
>
> to test if what I was doing made any sense I checked for IGF1 cell line for
> treatment glarg at 6h compared to its vehicle control, I also included an
> interaction term to test: "what is the difference of cell lines IRA and IRB
> in their response to glargine at 6h?
>
> > cont.matrix1 <- makeContrasts( IGF1_glarg_6 =
>                                   IGF1R.glargine.6h-IGF1R.control.6h,
>     >                            IRA_IRB_glarg_6h =  ( IRA.glargine.6h
> - IRA.control.6h
> ) - ( IRB.glargine.6h - IRB.control.6h ),
>        >                        levels = design )
>  >  fitAll2 <- contrasts.fit(fitAll, cont.matrix1)
>    >  fitAll3 <- eBayes(fitAll2)
>
> The results don't seem to make sense since the intersection of probe IDs
> from the toptable  results (number = 500) and the results from a simple t
> test between IGF1R.glargine.6h-IGF1R.control.6h (also 500 rows), samples is
> very low (random even)
>
> Any help to which manual examples I should look, or a general strategy is
> greatly appreciated.
>
> Best regards
>
> Steven Wink
>
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>
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