[BioC] using genomic DNA as universal reference

[Naomi Altman]

Actually, I do not think you can determine the suitability of loess 
normalization from a summary like this.  You need to look at the MA 
plots and see if the expression values are evenly spread around the 
central trend.  You might want to use hexbin to do the plots, because 
the central trend is usually very dense.

Personally, I do a lot of graphics before doing any analysis of 
microarray data.

--Naomi

At 05:01 PM 6/5/2008, Jianping Jin wrote:
>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
>

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