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Signal identification in large-dimensional settings is a challenging problemin biostatistics. Recently, the method of higher criticism (HC) was shown to bean effective means for determining appropriate decision thresholds. Here, westudy HC from a false discovery rate (FDR) perspective. We show that the HCthreshold may be viewed as an approximation to a natural class boundary (CB) intwo-class discriminant analysis which in turn is expressible as FDR threshold.We demonstrate that in a rare-weak setting in the region of the phase spacewhere signal identification is possible both thresholds are practicablyindistinguishable, and thus HC thresholding is identical to using a simplelocal FDR cutoff. The relationship of the HC and CB thresholds and theirproperties are investigated both analytically and by simulations, and arefurther compared by application to four cancer gene expression data sets.

Signal Identification for Rare and Weak Features: Higher Criticism or False Discovery Rates?