Dear Gordon,
I was always wondering about the same question since I am not a statistician. What I found reading on the internet is that MDS uses distances to calculate eigenvectors, so you can do MDS although you do not know the absolute coordinates but using the relative distances. But if you have the original values, then standard Principal Component Analysis and classical (metric) MDS should give the same components. But please correct me if I am wrong. That would explain why plotMDS and PCA score plots are that often that similar when used with limma.
Thanks for the posts. 
Jose








On May 23, 2013, at 1:53 AM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Manoj,
> 
> plotMDS does not do PCA.  As the documentation says
> 
> "This function is a variation on the usual multdimensional scaling (or 
> principle coordinate) plot".
> 
> Statisticians are sometimes not very imaginative when choosing names for 
> things, but PCA is an abbreviation for "principle component analysis" 
> which is not the same as "principle coordinate analysis".
> 
> Best wishes
> Gordon
> 
>> Date: Tue, 21 May 2013 11:55:36 -0700 (PDT)
>> From: Manoj Hariharan <h_manoj@yahoo.com>
>> To: "Bioconductor@r-project.org" <Bioconductor@r-project.org>
>> Subject: [BioC] edgeR MDS
>> 
>> Hello,
>> 
>> I am working on edgeR version 3.2.3.
>> 
>>> From the documentation, I guess the "plotMDS.DGEList" is similar to 
>>> PCA. The manual mentions that "Distances on the plot represent 
>>> coefficient of variation of expression between samples".
>> 
>> Is it possible to get a value of proportion of variance explained from 
>> each dimension (component)? Also, is it possible to use the DGEList to 
>> make a 3D PCA plot?
>> 
>> Thanks,
>> 
>> Manoj.
>> 
>>  
>> Manoj Hariharan
>> Staff Researcher
>> The Salk Institute for Biological Studies
>> La Jolla, CA 92037
>> Office: 858.453.4100 x2143
> 
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