[BioC] LimmaGUI Spot Quality

[Elizabeth Brooke-Powell]

Hi James,

The confidence values are give in numbers as decimals with 1 = 100%
confident (e.g. confidence value = 0.78) this is a value determined using
Bayesian statistics and is a measure of how confident the package is that
the spot it found is real. The package itself (BlueFuse only currently
available in the UK) uses a Bayesian model to iteratively find spots
looking. I don't know much more as it's protected, and I'm a biologist.  

Basically I am asking if the model can take account of these numbers and
adjust the model appropriately. I am not sure in this case that pretending
to have GenePix will work as the numbers are not a simple 0 or 1 (good or
bad). If I was to try this, do I need to format the txt file of data to look
like a GenePix file?

Thanks for you help,

Liz

-----Original Message-----
From: James Wettenhall [mailto:wettenhall at wehi.edu.au] 
Sent: 02 July 2004 14:02
To: Elizabeth Brooke-Powell
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] LimmaGUI Spot Quality

Hi Liz,

limmaGUI is not as flexible as limma when it comes to spot 
quality measures for "new spot finding packages".  Please tell 
us the column name(s) from your raw image-analysis results files 
which you want to use for assessing quality, and if you can 
explain what the quality indicator in this column means (e.g. 
high=good, low=bad, ...), that would be even better.

Try the limmaGUI spot-quality-weighting option for GenePix.  
(Even if you don't have any GenePix files, you can just 
pretend you do have GenePix files in order to see the 
spot-quality weighting dialog.)  You can give different weights 
to different GenePix flags (for "bad" spots or "not found" 
spots etc.)  Is this the sort of thing you are looking for?

The extra quality column(s) are read in when the raw data is 
read in, and then they are used to form weights in the 
normalization routines in limma.

Type:
?normalizeWithinArrays 
OR
?wtflags (not as flexible as the limmaGUI GenePix flags dialog)

at the R prompt for a bit more information.

Regards,
James
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