R help - Aug 2005 - Cases as Variables in Principal Component Analysis?

Dear R users,

I have a data set of 25 cases with 150-160 explanatory variables(the
number of which depends on what I choose from 200 odd digitalized spectrum
strength numbers) and one dependent variable(a sensory test result). My
natural choice is to work on a principal component analysis using the
explanatory variables, thus enabling to characterize and describe the
data space, and make a regression of the dependent variable on the
principal components. 

But a colleague of mine transposed the data matrix and, using the cases
as the independent variables, explained the dependent variable in terms
of the principal components he had. He changed obviously the score for the
rotation. The analysis gave a plausible story. But I can't be sure of the
physical meaning of it. 

My colleague says that this method is common in the image analysis
proper, which he specializes in. 

Is there anyone who can comment on this matter. Venables & Ripley says
something to the effect that either method will do, but the authors do
not seem to give a specific example.

In my trade(chemistry), the data is commonly analyzed by the PLS(Partial
Least Suare) method, which seems to give more or less the same result.
Only the contribution of the PC's seems to be different.

I would appreciate any help. Thank you.

-- 
Yukihiro Ishii <yukiasais at ybb.ne.jp>