[BioC] using genomic DNA as universal reference

[Jianping Jin]

Thanks Naomi for your reply to correct me!

I checked the data set on M values. The typical numbers are that 14K (even 
less) genes are larger than 0 and 28K genes are less than 0 before loess 
normalization. For this data set what you think about loess normalization?

best,

Jianping


--On Thursday, June 05, 2008 4:06 PM -0400 Naomi Altman 
<naomi at stat.psu.edu> wrote:

> Actually, the requirement for loess normalization is that differential
> expression is not dependent on expression level, and that up and down
> regulation are symmetric.
>
> --Naomi
>
>
> At 03:27 PM 6/5/2008, Jianping Jin wrote:
>> 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!
>>
>> Jianping
>>
>> --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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>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
>



##################################
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