hi james, briefly, to make new chips comparable to a training data set normalized with RMA you can do the following: normalize your training arrays keeping track of: (1) the means over the ranks used in quantile normalization (2) the probe effects estimated by the median polish procedure as the background correction is performed chip-by-chip, you can transform each test (future) array to be compatible to the training arrays (and the classifier) with the above information. f() then works roughly like that: * substitute the (ranked) test-expression values by the means over the ranks from (1) (you're normalized now) * calculate a chip-effect (for each probe set) via subtracting the probe effect from (2) from each probe set (you're done now) i can send you the code for the above, in case you are interested. all the best, dennis Naomi Altman wrote: > I would say that it depends on how you plan to use the classification function. > > If, in future, you will collect more samples, and use the > classification function to classify them, then you need to normalize > the test set the same way you will normalize the new arrays. > How you plan to do this may also affect how you normalize the training set. > > --Naomi > > At 02:53 PM 12/17/2006, Wolfgang Huber wrote: >> Hi James, >> >> this is a general problem of normalization methods that work by adapting >> arrays in a set to themselves, and not to an independent reference. >> >> Option 1 is indeed discredited when you want to get a fair estimate of >> classification rates, since it does not faithfully simulate the real >> application where you want to classify a new sample. >> >> Option 2 does not work since f contains for each array a number of >> array-specific, ideosyncratic parameters that reflect hybridization >> conditions, labeling efficiency, RNA extraction etc. You cannot "learn" >> them in advance. >> >> The option I'd take is to look for a normalization method that >> normalizes each new array individually (or in sets appropriate to your >> intended application) to an existing database of reference arrays. I >> know that various people on this list have been/are working on such >> methods. But I am probably not up-to-date myself - maybe someone can >> recommend? >> >> Best wishes >> Wolfgang >> >> ------------------------------------------------------------------ >> Wolfgang Huber EBI/EMBL Cambridge UK http://www.ebi.ac.uk/huber >> >> >>> Hi, I have a question for RMA normalization. Since RMA is an across >>> sample >> normalization, suppose I have 50 training samples (cel files) and 50 >> test samples (cel files). There are two ways to perform normalization: >>> 1. Combine all the 100 samples together and use RMA to do >> normalization. Then train the training set of 50 samples to classify the >> 50 test samples. >>> 2. Use the 50 training samples to do RMA, then each cel file is >> converted to gene expression vector. Suppose the mapping from cel file >> to expression vector is: >>> Expression = f(cel). The form of f is determined by the 50 training >> cel files. Then apply the same mapping to the test cel files. >>> I would think method 2 is more reasonable and trully blind. However, >> it is not clear how to determine the function f from the 50 training cel >> files. method 1 is easy to implement, but it is not trully blind, since >> the normalization of cel files from training samples actually utilized >> the information from test cel files. >>> Could anybody tell me how to determine the function f from the 50 >> training cel files? >>> Many thanks, James >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >