Question

Complex contrasts for between and within subject comparisons

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I have a question regarding the presented comparison both between and within subjects in chapter 3.5 of the edgeR users guide: I have two sets of samples, one set being sensitive to a compound treatment, the other resistant. For each group I have patient samples that were left untreated, treated 2h with a compound and 24h with a compound, so patient is clearly a blocking factor. This gives me the following design: design = model.matrix(~resist+resist:sub+resist:treat) (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 Now I understand that with the contrasts c(0,0,0,0,0,0,0,0,-1,1) and c(0,0,0,0,0,0,-1,1,0,0) you can test for differentially expressed genes between resistant and sensitive cells after 2h and 24h treatment, respectively. What is unclear to me is for example how to test for differentially expressed genes between resistant and sensitive cells in the control? It can???t be c(0,1,0,0,0,0,0,0,0,0), since that would give me differentially expressed genes between resistant and sensitive cells in any state right? The only workaround I have come up so far is to relevel the treatment factors and take treat2h as base level in order to be able to have now the control in the contrast in its position. What is the proper way to do this? -- output of sessionInfo(): Not needed since conceptual question -- Sent via the guest posting facility at bioconductor.org.

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ADD COMMENT • link updated 12 hours ago by wuwenhui2 • 0 • written 10.7 years ago by Guest User ★ 13k

score 0 · Answer 1 · 2014-07-18

Dear Tobias,

Multi-level experiments (with comparisons both between and within subjects) are quite complex to analyse.

edgeR can fit arbitrary linear models, but it cannot estimate multiple levels of variation. For an experiment such as yours, edgeR can test for effects within subjects, and it can test for interactions between subject groups for these effects. It cannot however test for marginal differences between the patient groups, because doing so would require estimating the variability of the patients as well as estimating variability of expression values within each patient.

For your experiment, you ask how to test for DE genes between resistant and sensitive cells in the control. The short answer is that you can't using edgeR. (Nor with other negative binomial based packages.)

I would suggest instead that you switch to limma-voom. This will allow you to estimate the intra-patient correlation (using duplicateCorrelation) instead of including subject in the design matrix. You will then be able to test any comparisons you wish between resistance and treatment groups.

The limma-voom approach to experiments such as yours has been discussed at some length on this mailing list, see for example:

https://stat.ethz.ch/pipermail/bioconductor/2014-May/059713.html

and the subsequent links.

Best wishes
Gordon