[R] PCA in Q- and R-modes
bgunter.4567 at gmail.com
Thu Jan 19 02:02:26 CET 2017
Off topic for this list.
Post on stats.stackexchange.com or similar for statistics questions.
Post on Bioconductor list for biology-related (e.g. proteomics) data
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Jan 18, 2017 at 10:35 AM, Josh Mitteldorf <agingadvice at gmail.com> wrote:
> I'm working with proteomic data, helping a student who knows biology and
> has done analysis in R without understanding it in depth.
> We have 3000 protein levels for 6 ages. I can treat this as 6 vectors in
> 3000-dimensional space, diagonalize a 6x6 covariance matrix and find 5
> principal components, one zero eigenvalue. My student has worked with R in
> "Q mode" and he enters the transposed matrix as 3000 vectors in
> 6-dimensional space. In just a few seconds, R diagonalizes a 3000x3000
> matrix! I can't imagine what that means, to diagonalize a 3000x3000
> matrix. But, of course, there are only 5 degrees of freedom in the data,
> so only 5 of the eigenvalues are non-zero, and the other 2995 vectors are
> Questions: a) Is there a relationship between the principal components
> of the 3000*6 matrix and the principal components of the transposed 6*3000
> b) Is there a way to find the 5 meaningful
> eigenvectors without carrying the baggage of diagonalizing the huge
> 3000-dimensional matrix?
> c) The big question is which version to analyze and
> publish? My student tells me the transposed matrix is the common
> procedure. The two yield very different-looking plots.
> Thanks for your help.
> - Josh Mitteldorf
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