Sva and batch effect
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Lenny186 • 0
@d0519f0c
Last seen 4 weeks ago
France

Hi,

I'm trying to understand how sva works and I have two pieces of codes and I want to be sure to understand the differences between them. I added # with my comment, but please if my terminology is wrong or informations are not correct, please correct it so I could understand better :

Code1:
library(limma)
mod <- model.matrix(~sex) #model could be explained by sex
svafit <- sva(geneExpression,mod)  # we suspect some batch effect but we don't know from which variable it comes, so we use sva
svaX<-model.matrix(~sex+svafit$sv) # we adjust with the sva news covariates lmfit <- lmFit(geneExpression,svaX)# we fit our original data with adjusted model tt<- lmfit$coef[,2]*sqrt(lmfit$df.residual)/(2*lmfit$sigma) # we calculated tt and then conclude with pvalues for sex variable
pvals=2*(1-pt(abs(tt),lmfit$df.residual[1]) Code2 library(limma) mod <- model.matrix(~sex+batch) # we explain our model with sex and some batch effect fit <- lmFit(geneExpression,mod) # we fit our model ses <- fit$stdev.unscaled[,2]*fit$sigma ttest <- fit$coef[,2]/ses # and conclude tt and then pvalue for sex variable
pvals <- 2*pt(-abs(ttest),fit\$df)

So when it comes to the conclusion : what is the difference between these two codes ? (Warning my answers could really be wrong but this is my understanding) : I would say the first code "remove" batch effect from the model then we can estimate more accurately sex effect. While the second to do not remove it but can allow to estimate the correspondant p-value of the batch effect, in the detrimental of sex effect estimation.

Please correct if wrong (surely :( )

Thanks a lot for your precious help

Lenny

limma BatchEffect sva • 183 views
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@james-w-macdonald-5106
Last seen 4 hours ago
United States

You shouldn't be computing regular t-statistics to begin with. In addition, sva is intended to adjust for unobserved variability, and a batch effect is clearly not that. You could hypothetically use ComBat if you think there is more to the batch than a shift in means, but in general the second way is how you should fit the model, but by following lmFit with an eBayes and then a topTable to extract the genes.

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(+1). I find that ComBat tends to introduce spurious differential expression so, you think there's a batch effect affecting the variances as well as means, then I'd recommend limma's array weights together with batch in the linear model rather than using ComBat.