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On 9/27/06, James Anderson <janderson_net at="" yahoo.com=""> wrote: > Hi, > This may be a generic question, not necessarily related to the usage of R and bioconductor. The question is: for microarray experiment, suppose I have 50 normal and 50 cancer samples. I want to find some sample outliers which may come from different resources due to: > 1. Mislabelling, i.e, mislabel cancer into normal or normal into cancer I think it is not an outlier problem but class noise problem. Google class noise correction or removal instead and there are some work on this topic. > 2. Misbehavior, i.e, some normal samples are actually sick or have heart attack, although they don't have cancer. If your problem is cancer vs non-cancer one, then again, they should not be removed either, IMHO. > > Should I do gene selection or not before doing outlier removal? Sometimes I find some samples are identified as outliers using 200 genes, other samples will be identified as outliers if I use 50 or 20 genes. Normally in microarray experiment, what is the percentage of genes affected by treatment or unnormal conditions? > > Thanks, > James > > > > --------------------------------- > Get your email and more, right on the new Yahoo.com > [[alternative HTML version deleted]] > > _______________________________________________ > 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 > -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III