[BioC] limma report logFC confidence interval?

Sun Jan 30 02:27:32 CET 2011

I have now included an option in topTable() to output 95% confidence 
intervals.  For now, it is available only in the version of limma on the 
Bioconductor developmental repository.

Best wishes
Gordon

> y <- matrix(rnorm(100*3),100,3)
> fit <- eBayes(lmFit(y))
> topTable(fit,confint=TRUE)
     logFC  CI.025 CI.975     t P.Value adj.P.Val     B
79 -1.555 -2.6345 -0.475 -2.82 0.00966     0.493 -4.50
11 -1.293 -2.2625 -0.323 -2.61 0.01555     0.493 -4.51
35 -1.251 -2.2000 -0.302 -2.58 0.01662     0.493 -4.51
97 -1.216 -2.1998 -0.231 -2.42 0.02376     0.493 -4.52
85 -1.171 -2.1252 -0.216 -2.40 0.02467     0.493 -4.52
41 -1.129 -2.1500 -0.108 -2.17 0.04073     0.538 -4.54
82  1.048  0.0944  2.002  2.15 0.04192     0.538 -4.54
49  1.057  0.0893  2.024  2.14 0.04308     0.538 -4.54
10  1.059  0.0402  2.077  2.04 0.05324     0.592 -4.55
60  0.916 -0.0653  1.898  1.83 0.08026     0.803 -4.56

On Fri, 28 Jan 2011, Fraser Sim wrote:

> Hi Gordon,
>
> I agree. This would be useful addition as some readers, especially
> non-statisticians, would like to see the relative errors of the logFCs on
> plots and tables.
>
> Cheers,
> Fraser
>
> -----Original Message-----
> From: bioconductor-bounces at r-project.org
> [mailto:bioconductor-bounces at r-project.org] On Behalf Of Richard Friedman
> Sent: Friday, January 28, 2011 11:21 AM
> To: Bioconductor mailing list
> Cc: Gordon Smyth
> Subject: Re: [BioC] limma report logFC confidence interval?
>
> 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) Educational Coordinator, Center for
> Computational Biology and Bioinformatics (C2B2)/ National Center for
> Multiscale Analysis of Genomic Networks (MAGNet) Room 824 Irving Cancer
> Research Center Columbia University
> 1130 St. Nicholas Ave
> New York, NY 10032
> (212)851-4765 (voice)
> friedman at cancercenter.columbia.edu
> http://cancercenter.columbia.edu/~friedman/
>
> "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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