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      Independent filtering increases detection power for high-throughput experiments.

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          Abstract

          With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by a criterion independent of the test statistic, and then only tests variables which pass the filter, can provide higher power. We show that use of some filter/test statistics pairs presented in the literature may, however, lead to loss of type I error control. We describe other pairs which avoid this problem. In an application to microarray data, we found that gene-by-gene filtering by overall variance followed by a t-test increased the number of discoveries by 50%. We also show that this particular statistic pair induces a lower bound on fold-change among the set of discoveries. Independent filtering-using filter/test pairs that are independent under the null hypothesis but correlated under the alternative-is a general approach that can substantially increase the efficiency of experiments.

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          Author and article information

          Journal
          Proc Natl Acad Sci U S A
          Proceedings of the National Academy of Sciences of the United States of America
          Proceedings of the National Academy of Sciences
          1091-6490
          0027-8424
          May 25 2010
          : 107
          : 21
          Affiliations
          [1 ] European Bioinformatics Institute, Cambridge CB10 1SD, UK.
          Article
          0914005107
          10.1073/pnas.0914005107
          2906865
          20460310
          ff4a66be-a359-4d2f-ab85-87bb65d1d98e
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