Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis

Jack Freestone, Lukas Käll, William Stafford Noble, Uri Keich

Abstract: Canonical procedures to control the false discovery rate (FDR) among the list of putative discoveries rely on our ability to compute informative p-values. Competition-based approach offers a fairly novel and increasingly popular alternative when computing such p-values is impractical. The popularity of this approach stems from its wide applicability: instead of computing p-values, which requires knowing the entire null distribution for each null hypothesis, a competition-based approach only requires a single draw from each such null distribution. This drawn example is known as a “decoy” in the mass spectrometry community (which was the first to adopt the competition approach) or as a “knockoff” in the statistics community. The decoy is competed with the original observation so that only the higher scoring of the two is retained. The number of decoy wins is subsequently used to estimate and control the FDR among the target wins.