[BioC] Background correction with just a few spots
January Weiner
january.weiner at mpiib-berlin.mpg.de
Fri Nov 12 11:14:58 CET 2010
Thank you very much for all the answers.
The data is ancient; those are the read-outs of spot intensities which
were transferred to a CSV. As mentioned, the labeling is radioactive,
so it is "single channel", and I just create the RG object manually
from data frames read into R, and just create the design matrix with
model.matrix() -- exactly the same procedure as the one I use for
single channel Agilent.
I will try to look into both, cellHTS and the nec() function.
Best regards,
January
On Thu, Nov 11, 2010 at 11:45 PM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
> 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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--
-------- Dr. January Weiner 3 --------------------------------------
Max Planck Institute for Infection Biology
Charitéplatz 1
D-10117 Berlin, Germany
Web : www.mpiib-berlin.mpg.de
Tel : +49-30-28460514
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