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Negative control is a common technique in scientific investigations andbroadly refers to the situation where a null effect (''negative result'') isexpected. Motivated by a real proteomic dataset, we will present threepromising and closely connected methods of using negative controls to assistsimultaneous hypothesis testing. The first method uses negative controls toconstruct a permutation p-value for every hypothesis under investigation, andwe give several sufficient conditions for such p-values to be valid andpositive regression dependent on the set (PRDS) of true nulls. The secondmethod uses negative controls to construct an estimate of the false discoveryrate (FDR), and we give a sufficient condition under which the step-upprocedure based on this estimate controls the FDR. The third method, derivedfrom an existing ad hoc algorithm for proteomic analysis, uses negativecontrols to construct a nonparametric estimator of the local false discoveryrate. We conclude with some practical suggestions and connections to someclosely related methods that are propsed recently.

Simultaneous Hypothesis Testing Using Internal Negative Controls with An Application to Proteomics