[BioC] Background correction with just a few spots

[Gordon K Smyth]

Dear January,

As Wei says, the neqc() function in limma has the effect of subtracting 
the mean intensity of the negative control or "background" spots from the 
other spots, before going on to do other normalization.  The nec() 
function gives you a bit more control if you don't want to do quantile 
normalization.  These functions can operate on the objects you get from 
read.maimages().  This is the way we'd recommend you to do it, although 
you could simply background subtract without the normexp step.

We could give more details in terms of code if you show us how you're 
reading the data in and what sort of data object you're creating.  For 
example, is the data one channel or two channel?

Best wishes
Gordon

> Date: Thu, 11 Nov 2010 08:37:35 +1100
> From: Wei Shi <shi at wehi.EDU.AU>
> To: January Weiner <january.weiner at mpiib-berlin.mpg.de>
> Cc: BioC <bioconductor at stat.math.ethz.ch>
> Subject: Re: [BioC] Background correction with just a few spots
>
> Dear January:
>
> 	The function neqc in limma package uses intensities from negative 
> control probes to perform a normexp background correction, followed by 
> quantile normalization and log2 transformation. For the details of this 
> method, please see the paper:
>
> http://nar.oxfordjournals.org/content/early/2010/10/06/nar.gkq871.abstract
>
> 	In brief, this method fits a normal+exponential convolution model 
> to the data but use the negative control probe intensities to estimate 
> the mean and standard deviation of background intensities.
>
> 	Let me know if you have any further questions.
>
> Cheers,
> Wei
>
> On Nov 10, 2010, at 7:56 PM, January Weiner wrote:
>
>> Dear all,
>>
>> I have a set of "strange" microarrays (nylon membrane / radioactive
>> labels). The raw data contains signals for the gene probes (a small
>> microbial genome) and for a number of probes which constitute the
>> background. There is no background signal directly in the data (like
>> in regular microarray chips), and I would like to subtract background
>> that is calculated from these few "background spots". Currently, I
>> just subtract the average of the background spots from all the other
>> spots.
>>
>> In limma, what would be the most appropriate way to do it?
>>
>> Cheers,
>> j.
>>
>> --
>> -------- Dr. January Weiner 3 --------------------------------------
>> Max Planck Institute for Infection Biology
>> Charit?platz 1
>> D-10117 Berlin, Germany
>> Web   : www.mpiib-berlin.mpg.de
>> Tel     : +49-30-28460514

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