[BioC] limma report logFC confidence interval?

[Richard Friedman]

Gordon,
	
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,
Rich
-----------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist,
Biomedical Informatics Shared Resource
Herbert Irving Comprehensive Cancer Center (HICCC)
Lecturer,
Department of Biomedical Informatics (DBMI)
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friedman at cancercenter.columbia.edu
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"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:

> Hi,
>
> I too would find it useful to have the CI reported.
>
> Thanks,
>
> Corrinne
>
> -----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  
> them,
> so the demand doesn't seem enough for now.
>
> Best wishes
> Gordon
>
>> 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  
>> my
>> $0.02.
>>
>> logFC is the coefficient of the treatment in your model. Assuming  
>> that you
>> have a model with only one treatment
>>
>> log Int_g = b0 + b1 * trt
>>
>> b1 = logFC
>>
>> The CI of logFC can be found in the same manner as you would do in  
>> normal
>> linear regression, but here instead of usual t(0.975, df) quantile,  
>> you
>> should use the moderated t quantile ie t(0.975, df.residual +  
>> df.prior)
>>
>> So the 95% CI for logFC will be
>>
>> logFC -+ t(0.975, fit3$df.residual + fit3$df.prior) *  
>> fit3$stdev.unscaled *
>> sqrt(fit3$s2.post)
>>
>>
>> Please correct me if I am wrong.
>>
>>
>> Thanks,
>> S.
>>
>> On Thu, Oct 28, 2010 at 7:46 AM, Timothy Wu <2huggie at gmail.com>  
>> wrote:
>>
>>> HI,
>>>
>>> Is there a way to report the CI of logFC from topTable in limma? I  
>>> googled
>>> around and it doesn't seem easy to find. I was expecting the  
>>> option to be
>>> in
>>> topTable().
>>>
>>> Thanks,
>>>
>>> Timothy
>
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