Hello Bioconductor Gurus, I have the a data about gene expression from TWO COLORED Agilent array. I wanted to check differential expression between p3 and wild strain of yeast. In one array p3 is colored with Cy5 and wild is colored with Cy3 and in the second array the dyes are swapped. Assuming I have normalized my data using VSN and obtained M values for the two arrays, I now want to use limma to derive the differentially expressed genes. My model matrix (say design) in this case will be p3 1 -1 if wild type is the reference. If my understanding is correct about how limma analyzes differential expression, then M value is the dependent variable, sample annotation (whether p3 or wild, provided by design) is the independent (explanatory) variable, and a linear model is fit per gene using the following equation. lmFit( M , design) As the data per gene is small, it is better to use eBayes method to obtain genewise p-value. But the object obtained from eBayes (say fit3) doesn't contain the value *logFC*. When I use topTable to order the genes, then logFC appears. The concept of logFC is clear to me in case of a Affy single colored array (ie log (Int_trt/ Int_control) ), but somehow I am still confused how to interpret this in two colored arrays. In my opinion M value (for each array) should represent logFC if color bias is ignored. How does limma derives its logFC value in two colored arrays? Is it based on the B statistics? Please enlighten me ! Thanks in advance for any help. Best Regards, S. [[alternative HTML version deleted]]