Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power Trey Ideker, Janusz Dutkowski, Leroy Hood  Cell  Volume 144, Issue 6, Pages 860-863 (March 2011) DOI: 10.1016/j.cell.2011.03.007 Copyright © 2011 Elsevier Inc. Terms and Conditions

Figure 1 Boosting Signal-to-Noise in Biological Data using Prior Knowledge (A) Signal detection paradigm in which an input data stream is routed through a series of filtering and integration units, ending in a statistical test that makes accept or reject decisions. Symbols: m, information per event or sample size; Δ, effect size; tα, decision threshold; FDR, false discovery rate. (B) Probability distribution P(t) of the test statistic t over the entire data stream of signal plus noise (purple). This distribution is factored into a red signal and a blue noise component. FDR and power are visualized in terms of the areas under these curves to the right of tα. (C) Effect of varying parameters on the signal, noise, and signal plus noise probability distributions. The power is increased by more than 6-fold compared to (B), at an identical FDR. Colors are shown as in (B). (D) MAGENTA, a specific implementation of the signal detection paradigm for pathway-based disease gene mapping as described in Segrè et al. (2010). Cell 2011 144, 860-863DOI: (10.1016/j.cell.2011.03.007) Copyright © 2011 Elsevier Inc. Terms and Conditions