**20**wrote:

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

**37k**• written 3.8 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.

ADD COMMENT
• link
•
modified 3.8 years ago
by
Gordon Smyth ♦ **37k**
•
written
3.8 years ago by
Vani • **20**

Answer: Standard error and effect size from Limma

5

3.8 years ago by

Gordon Smyth ♦ **37k**

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

Gordon Smyth ♦ **37k** 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.

Answer: Standard error and effect size from Limma

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.

Please log in to add an answer.

Use of this site constitutes acceptance of our User
Agreement
and Privacy
Policy.

Powered by Biostar
version 16.09

Traffic: 275 users visited in the last hour