Thanks for posting links to those articles, Guillermo. The use of train-test (cross-validation) and a validate split for evaluation of batch correction is fantastic. Something that is often underappreciated. I would like to try out qc-svrc, so I'll email you soon.
Jo, thanks for chiming in. That's an interesting point you make about RUVIII and something I'll keep an eye on.
It seems that linear models are being used by quite a few people, possibly with slight variations. I ran some simulations using varying RSD% and number of QC samples. Overfitting is a possibility if QC numbers are low. Interestingly, including data from randomized samples reduces this chance. Perhaps including a weighting parameter in favor of QC samples might be prudent.
It might interesting to hear what has/hasn't worked for others in the forum.
I'll throw in my 2 cents. Our lab has been experimenting with RUVIII (a variant of the 'PCA' method used by Wehrens). It can be found in the r package 'ruv' on cran. RUVIII estimates the factors of unwanted variation using replicate (QC) samples and internal standards (or control metabolites).
So far it has been pretty effective at reducing the coefficient of variation of QC samples (not the sample QC samples used above, but rather a validation set).
I'm getting used to XCMS3, but I hope I can help you anyway.
You might be getting unstuck due to differences in XCMSnExp and xcmsSet classes. Using the 'as' function, you can convert an XCMSnExp object to an xcmsSet object.
I think all of your code will work if you replace the first line with: xs.fill <- as(xgF,"xcmsSet")
Please let me (and everyone else) know if this works for you
