Thanks, Naomi.
   
  Now I understand where the difference comes from.  But how to measure the difference between groups to decide if to include them for analysis together?  In the five sample groups I mentioned, there are tumor samples as well as normal tissue (cell) samples.  I would like to know to which tissue (cell) the tumor is more similar.  Should I analyze them together or separately?
   
  Another question is about the p-value.  I used the p-value from affy control probes to select the p-value cutoff.  In the case of using 15 samples, the p-value for affy probes is 1e-7.  Does this indicate problems in the analysis?
  Thank you,
  Lisa

Naomi Altman <naomi@stat.psu.edu> wrote:
  The difference between your analyses comes from the denominator of 
the test. In both cases, the numerator is the differences in 
means. But in the first case, all of the samples are used to compute 
the within sums of squares, and all of these sums of squares are used 
in the limma ebayes adjustment. In the second case, only the 6 
samples were used to compute the within sums of squares.

Assuming that the groups have the about the same variance, the method 
using all 15 samples is more powerful (has a smaller error rate) and 
is preferable. If the 2 groups of interest have VERY difference 
variances, then you might we better off using just the 2 groups.

If you did gcrma first using all the data and then using only the 6 
samples, that would also contribute to the differences. Unless the 
groups are very different, my choice would be to use all the samples.

--Naomi


At 09:27 AM 4/20/2006, Lisa Luo wrote:
>Dear list,
> I am confused with my problem and hope get some help from you.
> I have 5 groups of sample, each with 3 samples (all AFFY). I 
> first read in all the 15 samples and did lmFit. I am interested in 
> the difference between group1 and group2, so I made a contrast 
> matrix with "group1-group2". Then I only read the 6 samples of 
> group1 and group2 and did the same thing. However, the 
> differentially expressed gene list are very different.
> I used gcrma to normalize the dataset. Do you think the 
> difference is caused by normalization or I did something wrong?
> Thanks,
> Lisa
>
>
>---------------------------------
>
> [[alternative HTML version deleted]]
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor@stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives: 
>http://news.gmane.org/gmane.science.biology.informatics.conductor

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



		
---------------------------------

	[[alternative HTML version deleted]]

