Is it possible to find the standard error and effect size of a dataset using limma's lmfit and toptable? Please advise.

Standard error and effect size from Limma

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Is it possible to find the standard error and effect size of a dataset using limma's lmfit and toptable? Please advise.

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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 usingYou are right. Now corrected.

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

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

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.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!

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

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