|Publication Type||Journal Article|
|Year of Publication||2009|
|Authors||Margolin, AA, Ong, SE, Schenone, M, Gould, R, Schreiber, SL, Carr, SA, Golub, TR|
Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein.