**20**wrote:

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

**34k**• written 3.1 years ago by Vani •

**20**

Question: Standard error and effect size from Limma

1

Vani • **20** wrote:

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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modified 3.1 years ago
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Gordon Smyth ♦ **34k**
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3.1 years ago by
Vani • **20**

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3.1 years ago by

Gordon Smyth ♦ **34k**

Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

Gordon Smyth ♦ **34k** wrote:

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.

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])

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.

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!

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.

4

Steve Lianoglou ♦ **12k** wrote:

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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