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Question: Evidence-based guidelines for use the method="robust" option in lmFit
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gravatar for Richard Friedman
6.5 years ago by
Richard Friedman2.0k wrote:
Dear list, I would greatly appreciate guidance on when to use the method="robust" option in lmFit. Use or non-use of the methods produces vastly different results on the set which I am studying. Below are reproduced 2 notes, from the list. The first from Gordon Smyth stating that there is no rule when to use it, and the second from Jenny Drenivch suggesting that sample size be a guide.. I would like to then ask the question a little differently: Has the effectiveness of using, method="robust" been evaluated in any study? The original Limma paper showed the superiority of Limma over the unmoderated t-statistic for ranking genes. Subsequent studies showed how Limma is superior to the t-test iusing simulation data. Has any paper compared the relative merits of robust and standard fitting in Limma? Or alternatively, has anyone ever obtained results with robust fitting that were validated independently but were not found by ordinary fitting? Here are the 2 previous posts: ########################################## Gordon Smyth smyth at wehi.edu.au Fri Apr 8 13:33:28 CEST 2005 * Previous message: [BioC] no BioConductor posting guide * Next messag Some people always like to use robust statistical methods when analysing microarray data, and the "robust" option to lmFit() is provided for this reason. There is no rule which tells you which data to use it for and which not. Gordon Jenny Drnevich drnevich at illinois.edu Thu Jan 8 18:58:21 CET 2009 * Previous message: [BioC] lmFit function * Next message: [BioC] lmFit function * Messages sorted by: [ date ] [ thread ] [ subject ] [ author ] Hi Priscila, The robustspline method for normalization has nothing to do with the lmFit(method="robust"). lmFit can either fit the model using a least squares regression or a robust regression, which down-weights replicates that are different from the other replicates. Whether or not to use lmFit(method="robust") doesn't depend on which normalization method you use, but rather (IMO) how many replicates you have. If you have a relatively large number of replicates, say 6 or more, then the robust fitting of the model may help to remove true outliers from affecting the data. However, if you only have 3 replicates, as is usual for microarray experiments, using the robust estimation may remove real variation in your samples and lead to more false-positives. That's my take on the situation... Jenny ########################################## Thanks and best wishes, Rich ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist, Biomedical Informatics Shared Resource Herbert Irving Comprehensive Cancer Center (HICCC) Lecturer, Department of Biomedical Informatics (DBMI) Educational Coordinator, Center for Computational Biology and Bioinformatics (C2B2)/ National Center for Multiscale Analysis of Genomic Networks (MAGNet) Room 824 Irving Cancer Research Center Columbia University 1130 St. Nicholas Ave New York, NY 10032 (212)851-4765 (voice) friedman at cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ I am a Bayesian. When I see a multiple-choice question on a test and I don't know the answer I say "eeney-meaney-miney-moe". Rose Friedman, Age 14
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