[BioC] Kooperberg introduces Nas??

Matthew Ritchie mritchie at wehi.edu.au
Mon Jun 7 03:13:52 CEST 2004

Hi Mick,

You're right, the model described in Kooperberg et al (JCB, Vol 9 No 1, 
2002) is supposed to avoid NA's, and if implemented as described in the 
paper it will.  My implementation has a line which sets the adjusted 
foreground values to missing if they are estimated to be off the scale 
(ie larger than 2^16).

This is possible when the denominator of equation (2) in the Kooperberg 
paper is very small, which occurs when the GenePix background is much 
larger than the foreground.  This causes the model estimate to blow up, 
giving a value which I'd argue is as useful as a negative value (hence 
the decision to set it to NA).  

If you send me the full details for the spot in question (eg fg, bg, 
fgSD, bgSD, and the number of foreground and background pixels), I can 
work out the model estimate for the foreground for you (which may be in 

The kooperberg() code has been modified to return an RGList object 
instead a list in the next update of limma.  It should be on the website 
in the next few days.

As for the hump-backed phenomena you mentioned in your earlier email, 
the MA plot will look slightly different depending on the amount of 
background you subtract.  If you take off a bit too much, you'll see a 
lot of fanning at low intensities, and not much of a RG bias.  If you 
don't subtract background, you tend to see a 'hump-backed' bias (ie the 
low intensity spots have slightly negative log-ratios).  This means that 
the low intensity spots have higher signal in the green channel than in 
the red, which is due in part to the substrate (glass slides tend to 
have an underlying 'greeness.')  The plotDensities(RG, 
log.transform=TRUE) function will give you a different view of this.  I 
guess the kooperberg() function will tend to give results more like the 
'no background' case than the 'too much background' which would explain 
what you observe.  The important point is that intensity based 
normalization should remove such biases.

Best wishes,

Matt Ritchie

michael watson (IAH-C) wrote:

>I have read in some data using the limma examples and everything is
>fine, I have used the kooperberg algorithm and everything works, APART
>from one single spot where I have an NA for some reason:
>[1] 255 919 371 315
>[1] 152 929 116  NA
>Now, the spot in question has a Cy3 foreground mean of 2256 and a Cy3
>background median of 4782.
>OK, so it is a bad spot.  But GenePix hasn't flagged it, perhaps because
>background is only high in the Cy3 channel - whatever.
>Anyway, I didn't realise that Kooperberg could introduce NAs into the
>data set - I thought it was this type of problem that kooperberg was
>meant to address? (ie negative spots).
>Thanks in advance for your help

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