hi to all bioconductor users! our goal is to find differentially expressed genes in cell line and patient probes with and without a given treatment. so usually we have two chips (we have not enough money to do replicates, i think that's fairly common with affymetrix ;) ), one chip has RNA with treatment, the other one without. i currently try to find out what normalization method gives us the best results, i am using mainly the mas5 method implemented in the affy package, the rma method and a "hybrid" version i call rma/mas, because bg correction is done with the rma method, quantiles is used as the normalization step pm signals are not corrected and finaly as the summary method i use mas5. is there any possibility to find out what for a method normalizes my data best? currently i am looking at the shape of the MA plots that i create from the normalized data. there the rma method looks best, no big variance in the lower expression level, but i have currently a problem with rma, because i am not shure how well the model parameter are fitted into the data, when i have only the data from two chips to calculate them. it looks good, better then mas5 and rma/mas. i am glad to have also a set of positive control genes, that i found by a literature search. so i can check if these genes are regulated in all of the methods, and they are. so from this point of view all methods work well. so i repeat my question: has anyone experience in analyzing affy chip with only two or four chips available? what methods for normalizing do you use, why? is there a way to find out (on my data without replicates) what method performs best? sorry for this very long description of my work, but i need some discussion, because i am the only bioinformatician in this lab :( thanks, jo