[BioC] using genomic DNA as universal reference

Jianping Jin jjin at email.unc.edu
Thu Jun 5 21:27:21 CEST 2008

Thanks Sean for your input!

the T-test result was used just for estimation of how many probe 
expressions were significantly different between RNA/DNA samples. This is 
also related to my normalization questions. According to my understanding 
the basic assumption for loess normalization is that most of the probes on 
the array are not differentially expressed. This is Agilent two-color data. 
Is loess normalization appropriate for such a different data on each array?

thanks again!


--On Thursday, June 05, 2008 1:28 PM -0400 Sean Davis 
<sdavis2 at mail.nih.gov> wrote:

> On Thu, Jun 5, 2008 at 12:31 PM, Jianping Jin <jjin at email.unc.edu> wrote:
>> 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?
> While not ideal, this sounds like a common reference design.  You
> could make use of normal two-channel normalization methods (centering,
> linear, or loess, etc.), use only single-channel data (and ignore the
> control), or use some of the single-channel normalization methods for
> two channel data described in the limma user guide.  I'm not sure that
> the t-test results are that important in making a decision.  Others
> might have more insight and (more importantly) more experience in this
> situation.
> Sean

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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