[BioC] About weight function for Agilent data

[Gordon K Smyth]

> Date: Thu, 17 Nov 2005 22:10:33 +0100
> From: Nataliya Yeremenko <eremenko at science.uva.nl>
> Subject: [BioC] About weight function for Agilent data
> To: Bioconductor List <bioconductor at stat.math.ethz.ch>
>
> I have seen here in the BioC topics several threads concerning weighting of
> the Agilent data, howeever I didn't understand how important it is,
> how does it influence linear models and differential expression testing.
>
> In particular Agilent Feature extraction performs quite a lot of
> flagging and do normalization itself.
> What kind of flags is important to set for weight zero?
> Should control spots be weighted zero as well?
> Is it wise to use processed intensities (and do not use withinarray
> normalisation of Limma) instead of raw data?
> There are number of between normalisations, but which one to use?
>
>
> --
> Dr. Nataliya Yeremenko
>
> Universiteit van Amsterdam
> Faculty of Science
> IBED/AMB (Aquatische Microbiologie)
> Nieuwe Achtergracht 127
> NL-1018WS Amsterdam
> the Netherlands
>
> tel. + 31 20 5257089
> fax  + 31 20 5257064

Control spots should be removed before the differential expression analysis.

Apart from that, my experience is that most flags estimated by image analysis programs are best
ignored.  They tend to be very conservative and to encourage you to remove data which is actually
quite usuable in the context of a replicated experiment.  However this is not based on any careful
analysis of AgilentFE's flags, so you may find differently.

If you are using limma's lmscFit function, you do not have the option of weighting or flagging
spots anyway.

Best wishes
Gordon