[BioC] Testing biased microarray data

[January Weiner]

Dear all,

the following problem: samples are either RNA, or RNA with selective
depletion of some forms of RNA. In short, the relative abundance in
the second group of samples should always be equal to or smaller than
that in the control, but never higher. The difference in abundance
might concern a substantial fraction of mRNAs (10-50%).

Naturally, when the samples are normalised, since the total transcript
abundance in the experimental group is significantly lower, the
relative abundance of transcripts with no change will be higher in the
experimental group, and artifacts will occur: we will observe genes
that are apparently up-regulated, although in reality their levels
remain stable.

The arrays are two-color Agilent chips. The preferred analysis tool
would be limma.

What would be the best way to normalise such data? I am considering,
at the moment, the following:
- completely forget between array normalisation, just doing the
background substraction and within array normalisation;
- use QPCR to measure several genes in a wide dynamic range, use them
to guide normalisation so that the differences in levels fit the
observed differences in QPCR

Any suggestions are appreciated,

Best regards,

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