Thank you for your reply. actually I have two biological replicates in my experiment, and I'm confident with the statistic power of limma with your information. gang jiang At 2011-05-17 00:15:14£¬"Naomi Altman" <naomi@stat.psu.edu> wrote: >If you have biological replicates, then using LIMMA is preferred with >small sample sizes. If you have only technical replicates, then you >really cannot do a proper statistical analysis of the data. Since >you have a disconnected design, you might use separate channel >analysis to simplify the comparisons you want to make. > >Regards, >Naomi Altman > > >At 07:40 AM 5/16/2011, =?GBK?B?va241Q==?= wrote: >>Hello Everyone! >> >> >>I'm now working with my expression microarray data by limma to >>detect differential expression probes. I have biology as my >>knowledge background ,not statistics, so I'm confused with the >>design matrix and contrast matrix in the limma usersguide. now i >>have read the target file >>as follows: >>SlideNumber FileName Cy3 Cy5 >>1 1 15_1_3.txt BI BM >>2 2 15_1_4.txt BM BI >>3 3 18_1_2.txt BI2 BM2 >>4 4 18_1_3.txt BM2 BI2 >>5 5 16_1_1.txt BE2 BM2 >>6 6 16_1_2.txt BM2 BE2 >> >> >>because there is no connection from BI(or BM) to the other samples, >>dose that mean I have to contrast the differences(BI-BM, BI2-BM2, >>BE2-BM2, BE2-BI2) separately? >>Though I read the linear models and Empirical Bayes Methods theory >>carefully, I only know little. I wonder It is proper to detect >>differential expression by limma when there are only two replicates? >> >> >> >> >> >> >> [[alternative HTML version deleted]] >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor@r-project.org >>https://stat.ethz.ch/mailman/listinfo/bioconductor >>Search the archives: >>http://news.gmane.org/gmane.science.biology.informatics.conductor > > > [[alternative HTML version deleted]]