Generally, the power is so low with only 3 replicates that you are better off using all the samples. What I do is plot all the log(expression) values verse each other using the "pairs" command in R. You should see a diagonal line near y=x on each plot, at least within treatment. The spread in the orthogonal direction is the important one for ANOVA. It should be about the same for each group. I have never used the affy control probes. Perhaps someone else can address this part of the question. At 11:02 AM 4/20/2006, Lisa Luo wrote: >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 at="" 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 at 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]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor at 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