Question

Limma: paired multifactor analysis

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gregory.l.stone ▴ 10

@gregorylstone-12225

Last seen 6.1 years ago

I am new to limma so please excuse my ignorance.

I am trying to use limma-voom to compare the effect of a treatment between males and females from an rna-seq experiment. I have paired samples, and my data looks as such:

sample	condition	sex	nested
sample1	pre	M	1
sample1	post	M	1
sample2	pre	M	2
sample2	post	M	2
sample70	pre	F	1
sample 70	post	F	1
...	...	...	...

I setup my design as such: design = model.matrix(~ sex + sex:nested + sex:condition, data), and then did fit <- lmFit(v, design) in accordance with the manual.

I'm confused as to how best proceed to make a contrast matrix that compares the effect of the condition between the sexes while accounting for the pairing. Any help would be much appreciated!

limma limma design matrix limma contrast matrix limma paired analysis limma-voom • 2.0k views

ADD COMMENT • link updated 7.8 years ago by Gordon Smyth 52k • written 7.8 years ago by gregory.l.stone ▴ 10

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What does "nested" mean here? You say that you want to compare the post vs pre treatment effect between the sexes, but how does "nested" come into this?

ADD REPLY • link 7.8 years ago Gordon Smyth 52k

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I apologize, I am answering from a different account since I seem to have reached a positing limit. Nested is numbering each sample within its sex group, as recommended by the edgeR manual in the section detailing making comparisons between and within groups.

I would like to use limma to contrast the change in gene expression due to the condition between the sexes, so this design seemed appropriate

ADD REPLY • link 7.8 years ago gstone2 • 0

score 1 · Answer 1 · 2017-02-07

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Gordon Smyth 52k

@gordon-smyth

Last seen 7 minutes ago

WEHI, Melbourne, Australia

I see that you are following Section 3.5 of the edgeR User's Guide. The last two coefficients in your fitted model correspond to the treatment effect in females and the treatment in effect in males. So you just need to take the contrast between the last two coefficients in your model. There are examples of this in Section 3.5.

I don't know how many columns your design matrix has or what the column names are, but the following should always work.

p <- ncol(fit)
cont <- rep(0, p)
cont[p] <- 1
cont[p-1] <- -1

Now feed 'cont' as the contrast matrix to contrast.fit().

PS. I assume that you have made sure that 'nested' is a factor. The design matrix should have more columns than there are sample IDs.

ADD COMMENT • link 7.8 years ago Gordon Smyth 52k

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Great, thank you. Yes, nested is a factor. However, since I have an uneven amount of males and females it is my understanding that I should remove columns from the design matrix that do not correspond to a sample. Am I correct in thinking this?

ADD REPLY • link 7.8 years ago gstone2 • 0

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Well, yes, if there are columns of the design matrix that are all zero, then you should remove them. This is not essential, however, because limma will detect and remove any superfluous columns for you automatically. You will get warning messages about non-estimable coefficients and about coefficients being set to NA.

ADD REPLY • link 7.8 years ago Gordon Smyth 52k