[BioC] Complex between and within sample design

[Tobias Neumann [guest]]

Hi,

I have a experiment design that mirrors what is written in the edgeR vignette in section 3.5 Comparisons Both Between and Within Subjects.

I have two groups of cell lines: Resistant to a drug and sensitive to a drug. For each cell line in these groups, I have a control, a treatment after 2h and treatment after 24h.

Following the vignette, I have renumbered the cell lines within each group and set up the nested design:

design = model.matrix(~resist+resist:sub+resist:treat)

design
   (Intercept) resistY resistN:sub2 resistY:sub2 resistN:sub3 resistY:sub3
1            1       0            0            0            0            0
2            1       0            0            0            0            0
3            1       0            0            0            0            0
4            1       0            1            0            0            0
5            1       0            1            0            0            0
6            1       0            1            0            0            0
7            1       0            0            0            1            0
8            1       0            0            0            1            0
9            1       0            0            0            1            0
10           1       1            0            0            0            0
11           1       1            0            0            0            0
12           1       1            0            0            0            0
13           1       1            0            1            0            0
14           1       1            0            1            0            0
15           1       1            0            1            0            0
16           1       1            0            0            0            1
17           1       1            0            0            0            1
18           1       1            0            0            0            1
   resistN:treat24h resistY:treat24h resistN:treat2h resistY:treat2h
1                 0                0               0               0
2                 0                0               1               0
3                 1                0               0               0
4                 0                0               0               0
5                 0                0               1               0
6                 1                0               0               0
7                 0                0               0               0
8                 0                0               1               0
9                 1                0               0               0
10                0                0               0               0
11                0                0               0               1
12                0                1               0               0
13                0                0               0               0
14                0                0               0               1
15                0                1               0               0
16                0                0               0               0
17                0                0               0               1
18                0                1               0               0

I can now calculate differentially expressed genes between resistant and sensitive cells in the 2h treatment easily:

glmLRT(fit,contrast=c(0,0,0,0,0,0,0,0,-1,1))

Same goes for the same question in the 24h treatment.

glmLRT(fit,contrast=c(0,0,0,0,0,0,-1,1,0,0))

>From the vignette and design matrix it is however unclear to me how to formulate contrasts for e.g. the question on differentially expressed genes between sensitive/resistant control cells.
The contrast c(0,1,0,0,0,0,0,0,0,0) would just give me differentially expressed genes between sensitive/resistant cells in any condition judging from the matrix. So how would I test this?

It's also unclear to me how to test for more complex questions e.g. differentially expressed genes between sensitive/resistant cells in the 24h treatment that are not differentially expressed between sensitive/resistant cells in the control. Would I simply have to combine the results from the 24h treatment test and remove any genes that also pop up in the control test?

Thanks for your help!

 -- output of sessionInfo(): 

sessionInfo()
R version 3.0.2 (2013-09-25)
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] splines   parallel  stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
 [1] genefilter_1.44.0       RColorBrewer_1.0-5      geneplotter_1.40.0
 [4] annotate_1.40.1         AnnotationDbi_1.24.0    lattice_0.20-29
 [7] Biobase_2.22.0          gplots_2.13.0           ggplot2_0.9.3.1
[10] edgeR_3.4.2             limma_3.18.13           DESeq2_1.2.10
[13] RcppArmadillo_0.4.200.0 Rcpp_0.11.1             GenomicRanges_1.14.4
[16] XVector_0.2.0           IRanges_1.20.7          BiocGenerics_0.8.0

loaded via a namespace (and not attached):
 [1] bitops_1.0-6       caTools_1.16       colorspace_1.2-4   DBI_0.2-7
 [5] dichromat_2.0-0    digest_0.6.4       gdata_2.13.3       grid_3.0.2
 [9] gtable_0.1.2       gtools_3.3.1       KernSmooth_2.23-12 labeling_0.2
[13] locfit_1.5-9.1     MASS_7.3-31        munsell_0.4.2      plyr_1.8.1
[17] proto_0.3-10       reshape2_1.2.2     RSQLite_0.11.4     scales_0.2.3
[21] stats4_3.0.2       stringr_0.6.2      survival_2.37-7    tools_3.0.2
[25] XML_3.98-1.1       xtable_1.7-3

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