[BioC] LIMMA vs. dChip
fhong at salk.edu
Mon Mar 14 20:09:09 CET 2005
> We normalized the same data set using RMA and a very similar procedure
> Normalization makes a huge difference, even though the correlation between
> the expression values, array by array, can be very close to 100%. This
> been found many times. The recent thread "RMA vs gcRMA" sheds some light
> on this problem. I suspect that much of the difference lies in the low
> expressing genes - but this does not mean that these genes are "absent".
I agree with Naomi, those low expressing genes might still present,
although the expressions are low. For RMA and GCRMA normalized data, some
low expressing data also agree well, while there is also discrepancy in
high expression part.
My confusion is what to do? Filtering genes with inconsistent result from
RAM and GCRMA, or filtering genes with low intensities (MAS5 call?) and
use one normalization result to draw conclusion?
> At 02:46 PM 3/7/2005, Adaikalavan Ramasamy wrote:
>>Your question is bit vague and you provide little information. I do not
>>think LIMMA has preprocessing capabilities for Affymetrix data.
>>1) How did you preprocess the data ?
>>2) How did you "analyse" your data in dChip ? What technique (e.g. fold
>>change, t-test, wilcoxon) did you use in dChip ?
>>3) How did you select the differentially expressed genes ? (e.g. via p-
>>value cutoff or biological significance).
>>One possibility is that you are using very different test statistics.
>>With 5 in each group, it is difficult to draw any conclusions as some
>>methods are more robust than others at small number of arrays.
>>Another is that you choose a threshold that includes a lot of noisy
>>gene. An extreme example is to select all genes with a p-value less than
>>1 in which case you get 100% agreement between the two methods.
>>And yet another, you may have made a coding/programming error somewhere.
>>On Mon, 2005-03-07 at 14:15 -0500, jun.yan.a at utoronto.ca wrote:
>> > Dear list member,
>> > I have a set of Affymetrix data of 10 arrays, HG_U133A, seperated into
>> > two groups of 5 arrays each. I processed the data using LIMMA and
>> dChip. For
>> > dChip, I used all the default setting. The resulted differential
>> > genes of the two have only less than 50% in common.
>> > Why the number of the overlapped genes of the two results is so low?
>> > any problems? Can anyone help me?
>> > Thanks in advance,
>> > Jun
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> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348 (Statistics)
> University Park, PA 16802-2111
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Fangxin Hong, Ph.D.
Plant Biology Laboratory
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
E-mail: fhong at salk.edu
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