Question

normalization affects drastically limma-voom + sva results

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aec ▴ 90

@aec-9409

Last seen 3.9 years ago

Dear all,

Depending on the normalization approach (none, quantile, TMM or DESeq2) applied to the limma-voom function, the number of surrogate variables found by SVA and number of differentially expressed genes changes a lot. My question is, does SVA replace the normalization step? For example, if the RNA-seq samples have different sequencing depths, is this a technical factor that SVA corrects for?

Thanks,

normalization sva limma-voom • 2.4k views

ADD COMMENT • link updated 6.8 years ago by Gordon Smyth 50k • written 6.8 years ago by aec ▴ 90

score 1 · Answer 1 · 2017-07-25

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Gordon Smyth 50k

@gordon-smyth

Last seen 1 hour ago

WEHI, Melbourne, Australia

No, sva does not take the place of normalization. It could ameliorate a problems slightly if you failed to normalize properly, but that is hardly the right way to go.

Note also that limma-voom always corrects for sequencing depth, even if you choose normalize="none".

It is not my experience that choosing between "TMM", "DESeq2" or quantile normalization drastically affects results from limma-voom.

ADD COMMENT • link 6.8 years ago Gordon Smyth 50k

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Gordon,

My problem is that I have been trying SVA with different normalization options as follows:

y=DGEList(counts=counts)
A<-rowSums(y$counts)
isexpr<-A>500
y=y[isexpr,keep.lib.size=FALSE]
yy=calcNormFactors(y)
mod <- model.matrix(~group, data=info)
mod0 <- model.matrix(~1, data=info)
#option 1
v=voom(counts=y,design=mod)
n.sv = num.sv(v$E,mod,method="leek")
n.sv
1
#option 2
v=voom(counts=y,design=mod, normalize="quantile")
n.sv = num.sv(v$E,mod,method="leek")
n.sv
1
#option 3
v=voom(counts=yy,design=mod)
n.sv = num.sv(v$E,mod,method="leek")
n.sv
1
#option 4
n.sv = num.sv(yy,mod,method="leek")
n.sv
57
#option 5
n.sv = num.sv(y,mod,method="leek")
n.sv
57
#option 6
n.sv = num.sv(dat,mod,method="leek")
n.sv
57

For option 6 I followed :https://www.bioconductor.org/help/workflows/rnaseqGene/. Why is it that the number of sv depends so much on applying 'voom' or not?

ADD REPLY • link 6.7 years ago aec ▴ 90

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It is completely incorrect to input counts to num.sv() as if they were microarray expression values. You have to use voom() or cpm() to convert the counts to a scale on which a microarray-type analysis makes sense.

When you do use voom(), you get the same number of surrogate variables regardless of what normalization method you used, just as I suggested to you would happen.

ADD REPLY • link 6.7 years ago Gordon Smyth 50k

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Ok, then option 5 is discarded. But what about the rest?

option 4 used TMM normalization and option 6 uses DESeq2 normalization, they both give 57 sv.

options 1,2,3 use voom and give 1 sv.

option 6 follows this code https://www.bioconductor.org/help/workflows/rnaseqGene/ :

dat  <- counts(dds, normalized = TRUE)
idx  <- rowMeans(dat) > 1
dat  <- dat[idx, ]
mod  <- model.matrix(~ dex, colData(dds))
mod0 <- model.matrix(~   1, colData(dds))
svseq <- svaseq(dat, mod, mod0, n.sv = 2)

ADD REPLY • link 6.7 years ago aec ▴ 90

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Option 4 is counts. Option 5 is counts. Option 6 is counts. They are all incorrect. Option 6 is not the same as the rnaseq workflow.

If you use voom, you get the correct result ( n.sv=1 ).

If you ignore voom and just use counts, you get the wrong result ( n.sv=57 ).

That's pretty clear, is it not?

ADD REPLY • link 6.7 years ago Gordon Smyth 50k

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You are right Gordon, if I add this step:

logcpm <- cpm(yy, prior.count=2, log=TRUE)
n.sv = num.sv(logcpm,mod,method="leek")
n.sv
[1] 1

I got the same results as 'voom'.

If I want to calculate n.sv using the rnaseq DESeq2 worklow, how to do it? it is not specified. Should I use the 'rlog' or 'vst' transformations?

ADD REPLY • link 6.7 years ago aec ▴ 90