[BioC] using genomic DNA as universal reference

[oliveros at cnb.csic.es]

Jin,

Yes, I assume that gDNA distributions are comparable between arrays. I
also assume that the RNA channel distributions are similar too. BUT I can
not assume that both types are also similar (this is the main point).

I am not sure that Gquantile will be correct here because that method
modifies also the Red channel (in order to leave logRatios unchanged)...

I just make two separate tables, one for each type of sample, and
normalize them separately (I use normalize.quantiles from Affy package
with each table).


Regards,

Juan Carlos Oliveros
http://bioinfogp.cnb.csic.es


> Dear Juan,
>
> Thanks for sharing your experience with me! It is helpful.
>
> So when you are comparing data sets of interest that are collected from
> different experiments or at different time your assumption is that gDNA
> empirical distribution should be the same. The method, like
> "normalizeBetweenArrays" with method=Gquantile, can be applied to
> normalize
> all arrays. Is that correct?
>
> best,
>
> Jianping
>
>
> --On Thursday, June 05, 2008 7:52 PM +0200 oliveros at cnb.csic.es wrote:
>
>> Dear Jin,
>>
>> I used to work with this kind of data in the past: RNA in one channel
>> and
>> genomic DNA (gDNA) in the other. We used the gDNA as a reference value
>> for
>> each gene to quantify the amount of DNA present in each spot.
>>
>> I also noticed that the distribution of the intensities was different in
>> both types of samples. In fact this is expectable as the amount of mRNA
>> molecules in one sample has nothing to do with the amount of gDNA for
>> the
>> same gene in other sample.
>>
>> So I normalized the data separately:
>>
>> -I created two tables, one with all RNA values and other with all gDNA
>> values.
>>
>> -I adjusted the quantiles of each table separately.
>>
>> -Then I calculated the ratio RNA intensity / gDNA intensity and I used
>> this ratio RNA/gDNA as the expression value of the genes. In further
>> analysis steps I treated them as data coming from single channel
>> hybridizations.
>>
>>
>> I hope that helps.
>>
>> best,
>>
>> Juan Carlos Oliveros
>> Head of BioinfoGP Unit at CNB-CSIC
>> Madrid, Spain
>> http://bioinfogp.cnb.csic.es
>>
>>
>>> Dear list,
>>>
>>> I would like to ask comments and suggestions on how to normalize
>>> microarray
>>> data with genomic DNA as reference.
>>>
>>> The experiments were performed with bacterial RNA and genomic DNA
>>> samples. What I noticed was that the data were pretty consistent across
>>> all chips on
>>> both channels.  But there exists a huge difference between the two
>>> channels
>>> in terms of the distribution of the probe intensities, although the
>>> average
>>> intensities were the same for the both channels. T statistics with
>>> non-normalized data showed that there were two thirds probes with p
>>> values <= 0.05 by comparing the hybridization intensities between red
>>> and green channels.
>>>
>>> Regarding to the huge difference described above the normalization
>>> methods people usually use may not be appropriate for the RNA/DNA data
>>> sets. What normalization algorithms would be useful if there is any?
>>> Does anyone have experience with this?
>>>
>>> Any comments or suggestions will be appreciated!
>>>
>>> Jianping Jin
>>>
>>>
>>> ##################################
>>> Jianping Jin Ph.D.
>>> Bioinformatics scientist
>>> Center for Bioinformatics
>>> Room 3133 Bioinformatics building
>>> CB# 7104
>>> University of Chapel Hill
>>> Chapel Hill, NC 27599
>>> Phone: (919)843-6105
>>> FAX:   (919)843-3103
>>> E-Mail: jjin at email.unc.edu
>>>
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>>>
>>
>>
>
>
>
> ##################################
> Jianping Jin Ph.D.
> Bioinformatics scientist
> Center for Bioinformatics
> Room 3133 Bioinformatics building
> CB# 7104
> University of Chapel Hill
> Chapel Hill, NC 27599
> Phone: (919)843-6105
> FAX:   (919)843-3103
> E-Mail: jjin at email.unc.edu
>
>