[BioC] What to do with this data? Question on deconfouding and GO analysis

[January Weiner]

Hello,

I've been asked to analyze data from the following experiment.

Two types of cells were analyzed either separately (A, B) or in a
mixture (AB). In each experiment, either the separated cell types or
the mixture was subjected to a treatment. From each such experiment, a
single Agilent two-color microarray was prepared, with untreated cells
used as a control.

Of course, proper significance analysis cannot be done, and I can only
use the technical p-values generated by the Agilent software. Due to
the nature of the experiment, it is unlikely that another data set can
be generated in a foreseeable future. However, the results in general
show the expected response to treatment and activation of a number of
genes that are supposed to be activated; thus, the technical p-values
still give a meaningful "general picture".

By manually going through the data it is obvious that in many cases,
the response in AB is a weighted average of the responses A and B. I
tried to estimate this global weights in a very naive manner, by
looking at the correlation between the fold change in experiment AB,
and the fold change estimated from experiments A and B for different
values of p, the proportion of cells of type A in the mixture AB.

My first question is therefore -- is there a recommended solution
within Bioconductor that I could apply in such a case?

Furthermore, I'd like to look for an interaction effect -- to predict
genes, GO terms or pathways that behave "not according to predictions"
in the mixture AB. For this, I assume that the technical p-values are
meaningful (because I do not have another choice), and run a GO / SPIA
analysis on the three microarrays separately. Then, I manually look
through the results to find enriched terms which are different for the
AB experiment.

I wonder whether there is a possibility to compare results of two
GO-analyses. One could, for example, look for changes in rank
positions of different GO terms (since the p-values in such a set up
would probably be not very meaningful).

Thanks in advance for any help, suggestions, material for further reading etc.,

j.

-- 
-------- Dr. January Weiner 3 --------------------------------------
Max Planck Institute for Infection Biology
Charitéplatz 1
D-10117 Berlin, Germany
Web   : www.mpiib-berlin.mpg.de
Tel     : +49-30-28460514