I once has a paper returned for lack of CIs and I got out of it by
explaining that Limma didn't give them
Still it would be helpful if they were available as an option. Often
the experimentalists I support want
"error bars" (whatever those bars mean) rather than p-values or fdrs.
Thanks and best wishes,
Richard A. Friedman, PhD
Associate Research Scientist,
Biomedical Informatics Shared Resource
Herbert Irving Comprehensive Cancer Center (HICCC)
Department of Biomedical Informatics (DBMI)
Center for Computational Biology and Bioinformatics (C2B2)/
National Center for Multiscale Analysis of Genomic Networks (MAGNet)
Irving Cancer Research Center
1130 St. Nicholas Ave
New York, NY 10032
friedman at cancercenter.columbia.edu
"Did he win the Nobel prize or the Ig Nobel
prize for levitating the frog?".
Rose Friedman, age 14
On Jan 28, 2011, at 11:13 AM, Segal, Corrinne wrote:
> I too would find it useful to have the CI reported.
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch [mailto
:bioconductor-bounces at stat.math.ethz.ch
> ] On Behalf Of Gordon K Smyth
> Sent: 30 October 2010 00:13
> To: Timothy Wu
> Cc: Bioconductor mailing list
> Subject: [BioC] limma report logFC confidence interval?
> Sunny's CI is exactly right.
> CIs could be an option in topTable(), but this the first request for
> so the demand doesn't seem enough for now.
> Best wishes
>> Date: Fri, 29 Oct 2010 00:14:39 -0400
>> From: Sunny Srivastava <research.baba at="" gmail.com="">
>> To: Timothy Wu <2huggie at gmail.com>
>> Cc: bioconductor <bioconductor at="" stat.math.ethz.ch="">
>> Subject: Re: [BioC] limma report logFC confidence interval?
>> Hello Thomas,
>> I am sure senior members of the list will have more to say, here is
>> logFC is the coefficient of the treatment in your model. Assuming
>> that you
>> have a model with only one treatment
>> log Int_g = b0 + b1 * trt
>> b1 = logFC
>> The CI of logFC can be found in the same manner as you would do in
>> linear regression, but here instead of usual t(0.975, df) quantile,
>> should use the moderated t quantile ie t(0.975, df.residual +
>> So the 95% CI for logFC will be
>> logFC -+ t(0.975, fit3$df.residual + fit3$df.prior) *
>> fit3$stdev.unscaled *
>> Please correct me if I am wrong.
>> On Thu, Oct 28, 2010 at 7:46 AM, Timothy Wu <2huggie at gmail.com>
>>> Is there a way to report the CI of logFC from topTable in limma? I
>>> around and it doesn't seem easy to find. I was expecting the
>>> option to be
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