It would be more accurate to say that the Li-Wong code in the affy BioC package was a best guess attempt at the time to implement the method at the time it was written (which is quite some time ago when no code was available). Which is not to say it did well then, or reflects any subsequent changes in dChip since then. Henrik is correct in that the pre-processing will also have an effect on your comparison. Although I think recent versions of dChip do provide a quantile normalization implementation it is not the default, but the description below about using splines is accurate for the default method. In the affy package this is reflected in "invariant" normalization method (?normalize.AffyBatch.invariantset). Also last I checked the dChip background was similar to what is done in MAS5 but in a 10 by 10 grid rather than 4 by 4. Best, Ben > > > > James, > > > > I understand your point and I personally prefer open source software... > > > > I am using own arrays and arrays from public databases which have been > > analyzed with dChip. Since I use R and I found different results, I am > > trying to > > understand where the differences come from and if this can affect the > > biology. > > You point is most important. It is actually not quite the case that > dChip is a black box. Under "Source code and command line version" on > > http://biosun1.harvard.edu/complab/dchip/install.htm > > it says "the latest source codes of dChip are available on request [by > sending an email]". Also, the source code for a version of dChip for > "Linux/MPI", which I assume has some in common with the Windows > version, is available for download (see link on the above page). > > BTW, what kind of preprocessing do you apply in your comparison? For > instance, both dChip and R/BioC provide quantile normalization but > they use totally different algorithms (and model assumptions). To the > best of my understanding (from browsing the Linux dChip code), dChip > uses splines, whereas R/BioC uses sorting for quantile normalization, > which in practice means that dChip fits a smoother function and when > comparing empirical density functions of normalized probe signals they > will not be identical across arrays whereas the R/BioC normalized ones > will be. > > /Henrik