Signal identification for rare and weak features: higher criticism or false discovery rates?

Biostatistics. 2013 Jan;14(1):129-43. doi: 10.1093/biostatistics/kxs030. Epub 2012 Sep 6.


Signal identification in large-dimensional settings is a challenging problem in biostatistics. Recently, the method of higher criticism (HC) was shown to be an effective means for determining appropriate decision thresholds. Here, we study HC from a false discovery rate (FDR) perspective. We show that the HC threshold may be viewed as an approximation to a natural class boundary (CB) in two-class discriminant analysis which in turn is expressible as the FDR threshold. We demonstrate that in a rare-weak setting in the region of the phase space where signal identification is possible, both thresholds are practicably indistinguishable, and thus HC thresholding is identical to using a simple local FDR cutoff. The relationship of the HC and CB thresholds and their properties are investigated both analytically and by simulations, and are further compared by the application to four cancer gene expression data sets.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biometry / methods*
  • Computer Simulation
  • False Positive Reactions
  • Gene Expression Profiling / methods*
  • Humans
  • Models, Statistical*
  • Research Design