Dear all and kevin,

The target of the experiement is to try to find genes that are specific for
embryo development and are downregulated in the adult tissues. For this I
use a pool of different adult tissues for different organs and a pool of
arrays for different mouse embryo stages.

If I process independently  the  embryo and the adult tissues, rescale the
expression levels to the same median, the distributions looks pretty
similar, although the number of expressed geens may vary, which I guess it
is due to the lower amount of DNA used for the  embryo arrays. But since I
look at downregulation from embryo to adult this should not be a major
problem.


I tried to normalize all the arrays together using invariant set and MBEI
method.
Although the variance of the probes expression gets very similar, the
medians of the embryo arrays are
somehow lower, which probably produces a lot of false positives when looking
at differential expression.

I would like to try to normalize within arrays and compare the results as
suggested from the dCHIP guys:

1. normalize on embryos and adults separately
2. rescale to the same average median expression
3. run summary (for example liwong) on all the arrays together.

Can this be done in R? Is it possible just to normalize the arrays without
getting expression summary? Seems
that expresso produces a summary expression anyway. Is it possible then to
run expression summaru method on
the normalized data?

If anybody has thoughts, comments or references about this, I would be
really happy to hear!

Marco








On 3/26/07, Kevin R. Coombes <krc@mdacc.tmc.edu> wrote:
>
> Hi,
>
> Most normalization methods assume (either explicitly or implicitly)
> something about the distributions of values within each sample.  At the
> very least, they assume that some properties of these distributions are
> the same.  The case of mouse embryos and adult tissues illustrates one
> of the places these assumptions are likely to be violated.  For instance,
>         [1] is the number of expressed genes the same?
>         [2] are there roughly equal numbers of high, medium, and low
> expressing
> genes?
>         [3] is the median expression level the same?
>         [4] is the total RNA per cell the same?
>
> Since genes are getting turned on and off at various rates in the
> development from embryo to adult, and since embryonic cells are actively
> proliferating and differentiating in ways that normal adult cells are
> not, it seems to me that there is no good reason to believe (without
> experimental data supporting it) that anything about the distributions
> are the same.
>
> If you normalize within (but not between) the two data sets, you should
> be able to start to answer questions [1] and [2].  If the answers are
> "yes", then it makes sense to start thinking about how to normalize
> between the two data sets....
>
>         Kevin
>
> marco zucchelli wrote:
> > Hi,
> >
> > I would like to  check for differential expression on arrays
> > downloaded from public databases.
> > I was reading somewhere that it might be not very wise to normalize
> > together arrays produced from different tissues and different labs.
> >
> > In practice I would like to compare mouse embryos from one experiment
> > with mouse adult tissues from another experiment.
> >
> > Does somebody have an opinion on this ?
> >
> > I tried to normalized the adult arrays and the embryo arrays
> > separately, rescale them to the same median and then extract the
> > expression values from all the arrays together (with li.wong method).
> > I tried also to normalize and extract the expression values for all
> > the arrays together with the same method. The results seem to be
> > pretty similar.
> >
> > In this case the amplification protocols used are different since
> > embryo material is very small..
> >
> > Best regards
> >
> >
> > Marco
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>

	[[alternative HTML version deleted]]

