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Shrunken p ‐Values for Assessing Differential Expression with Applications to Genomic Data Analysis
Author(s) -
Ghosh Debashis
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00616.x
Subject(s) - computer science , inference , false discovery rate , multiple comparisons problem , estimator , data mining , expression (computer science) , profiling (computer programming) , statistical hypothesis testing , machine learning , data science , artificial intelligence , statistics , mathematics , biology , gene , biochemistry , programming language , operating system
Summary In many scientific problems involving high‐throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p ‐values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision‐theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.