Standard error and effect size from Limma
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Vani ▴ 20
@vani-8145
Last seen 9.0 years ago
United States

Is it possible to find the standard error and effect size of a dataset using limma's lmfit and toptable? Please advise.
effect size Limma toptable Standard Error • 8.6k views
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@gordon-smyth
Last seen 10 hours ago
WEHI, Melbourne, Australia

The effect sizes are contained in fit$coefficients.

The standard errors can be obtained from

SE <- sqrt(fit$s2.post) * fit$stdev.unscaled

This gives a matrix containing standard errors for every coefficient and every gene.
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I am confused; shouldn't that be sqrt(fit$s2.post)? Similar to the line 255 of the toptable.R file in limma source package, the confidence intervals are calculated using 

sqrt(eb$s2.post[top])*fit$stdev.unscaled[top,coef]*qt(alpha,df=eb$df.total[top])
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You are right. Now corrected.

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I'm sorry, just tried this code, but in my `fit` object I don't have 's2.post'. How do I get this?

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If you've run eBayes() on the fit object, then you will haves s2.post.

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However, I'm following this protocol here: https://molepi.github.io/DNAmArray_workflow/06_EWAS.html#correct_for_bias_and_inflation to run a EWAS. So, we start with limma(), but correct for inflation using bacon(). And thus, this is my code. 

designp <- model.matrix(metadata(Mvalues)$formula, data = colData(Mvalues))
datap <- assays(Mvalues)$data
fitp <- limma::lmFit(datap, designp)

Given that tstatp <-fitp$coef/fitp$stdev.unscaled/fitp$sigma had gotten me the T-statistic, I assumed fitp$stdev.unscaled/fitp$sigma was equal to standard error. And I assumed that fitp$coef would give me the effect sizes. If I don't run eBayes() where do I get the standard error from? Or is that effectively not possible?

Thanks!

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If you want to ask questions about a non-standard workflow, you should start a new post with the appropriate tags. (In this case, it doesn't seem to be a Bioconductor package, so you might as well ask the authors directly.) The workflow in question is a bit bemusing as limma is run without EB shrinkage, which defeats the purpose - you might as well use lm.fit. Anyway, fitp$stdev.unscaled*fitp$sigma is the standard error of the coefficient.

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Ah, thank you for the answer, this was not immediately clear to me.

 

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@steve-lianoglou-2771
Last seen 22 months ago
United States

Yes.

Calling topTable(fit, ..., confint=TRUE) provides you with the effect size (logFC) and 95% confidence intervals (CI.L, CI.R), from which you can back calculate the standard error, if need be.
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Cool thanks. Just a quick question: How would I calculate the standard error from the confidence intervals?

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I'd say, `(CI.R - CI.L)/3.92` (since the 95% confidence intervals should be plus/minus 1.96se)

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