[BioC] Using limma for quantitative proteomics data

[Yong Li]

Dear Axel,

thanks for your answer. You are right, a matrix can be given to
lmFit() and in this case just the Amean is not calculated in the
returned object.

Best regards,
Yong

On Tue, Jun 26, 2012 at 8:19 AM,  <axel.klenk at actelion.com> wrote:
> Dear Yong,
>
> I don't think you need an MAList -- all limma functions will accept a
> simple matrix
> of your log2 ratios... or at least, all limma functions I have ever used,
> will do that... :-)
>
> Cheers,
>
>  - axel
>
>
> Axel Klenk
> Research Informatician
> Actelion Pharmaceuticals Ltd / Gewerbestrasse 16 / CH-4123 Allschwil /
> Switzerland
>
>
>
>
> From:
> Yong Li <mail.yong.li at googlemail.com>
> To:
> bioconductor at r-project.org
> Date:
> 26.06.2012 00:01
> Subject:
> Re: [BioC] Using limma for quantitative proteomics data
> Sent by:
> bioconductor-bounces at r-project.org
>
>
>
> Dear Aaron,
>
> thank you and others for suggestions. My data is really ratios and not
> absolute values for normal and tumor. Sorry that I am still not quite
> sure how to move forward with limma when I take log2 of the ratios. It
> looks like I then will have the M component of the MAList, but how can
> I construct the A to make an MAList? Or I am missing something here?
>
> Kind regards,
> Yong
>
> On Tue, Jun 19, 2012 at 11:09 PM, Aaron Mackey <amackey at virginia.edu>
> wrote:
>> There's a thread on the bioconductor mailing list about using voom for
>> RSEM-based RNA-seq quantification, in which  Gordon Smythe explained
> that
>> while voom() was designed for count data, it doesn't require it.  As Tim
>> Triche has suggested, if you're raw data is really ratios (and not
> absolute
>> values for normal and tumor), then you should take log2 of those ratios
> and
>> use limma from there; you can then also hijack the arrayQualityMetrics
>> package to check QC (MA plots, mean-variance relationships, etc.)
>>
>> -Aaron
>>
>> On Tue, Jun 19, 2012 at 3:39 PM, Yong Li <mail.yong.li at googlemail.com>
>> wrote:
>>>
>>> Dear Aaron,
>>>
>>> thank you for your quick answer! I have checked the help page of
>>> voom() but it seems to be used for count data. My data are just
>>> tumor/normal ratios. I am wondering if you could provide more details?
>>>
>>> Best regards,
>>> Yong
>>>
>>> On Tue, Jun 19, 2012 at 8:18 PM, Aaron Mackey <amackey at virginia.edu>
>>> wrote:
>>> > yes, it should be possible with a voom()-based analysis to get the
>>> > variances
>>> > "right".
>>> >
>>> > -Aaron
>>> >
>>> > On Tue, Jun 19, 2012 at 12:47 PM, Yong Li
> <mail.yong.li at googlemail.com>
>>> > wrote:
>>> >>
>>> >> Hello,
>>> >>
>>> >> limma has been so valuable in microarray data analysis, but has
> anyone
>>> >> used limma for finding differentially expressed proteins from
>>> >> quantitative proteomics data? The data I got has tumor/normal ratios
>>> >> of thousands proteins, and both tumor and normal have a number of
>>> >> replicates. Could such data be analyzed with limma?
>>> >>
>>> >> If limma can not be used here, what statistics method is suitable so
>>> >> that we can get statistically significant proteins with p-values?
> Any
>>> >> suggestion is appreciated.
>>> >>
>>> >> Kind regards,
>>> >> Yong
>>> >>
>>> >> _______________________________________________
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>>> >
>>> >
>>>
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>>
>>
>
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