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We present a reformulation of the Benjamini-Hochberg method that is useful in 'large-scale' multiple testing problems based on discrete test statistics and derive its basic asymptotic (as the number of hypotheses tends to infinity) properties, subsuming earlier results. A set of gene expression data is used to illustrate the workings of the method in a multiple testing problem based on Kolmogorov-Smirnov and Mann-Whitney statistics.

The Benjamini-Hochberg Method in the Case of Discrete Test Statistics