[BioC] negative expression values

[michael watson (IAH-C)]

Hi Arne

There are several approaches:

1) create a positive lower threshold below which you do not believe
anything is being expressed.  Some people use 1, others use 50, but
essentially you do not "shift" your distribtion, you simply re-set all
values below a threshold to that threshold

2) perform a linear shift of the distribution - essentially, one takes
the lowest value and adds this to every value in the distribution.  

3) perform a slightly more intelligent shift - I believe there are some
functions in the R library vsn, but I have never used them.  These
result in an avoidance of zero or negative values (I think)

4) perform kooperberg background correction, which is available in the
limma package.  This is a published method which estimates low
expression values from negative numbers using some kind of Bayesian
magic ;-)

Thanks
Mick

-----Original Message-----
From: Arne.Muller at aventis.com [mailto:Arne.Muller at aventis.com] 
Sent: 10 June 2004 10:25
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] negative expression values


Hello,

I'm analysing a set of expression data that were processed (normalized)
in Rosetta Resolver version 4. I've exported the data into R to run
linear model + anova with 3 factors.

In Resolver expression values can be zero or negative - about 4 percent
of the probesets in my data set.

I'd like to work with log2 transformed intensities, so I was wondering
if there's anything speaking against shifting intensities, so that the
minimal intensity is e.g. 1. The actual minimmal value is -106 and the
maximum is about 14000.

	I'm happy to receive suggestions or comments,

	+regards,

	Arne

--
Arne Muller, Ph.D.
Toxicogenomics, Aventis Pharma
arne dot muller domain=aventis com

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