Scientific Publications

Boosting permutation tests for marker selection

Publication TypeJournal Article
AuthorsGad Getz, Joshua Gold, Stefano Monti
AbstractIn this paper, we present a general method for the efficient estimation of p-values based on permutation tests, and we apply it to the task of marker selection from gene expression data. The proposed algorithm is tailored to the problem of testing several thousands hypotheses of which only a small proportion is expected to be rejected. It works by iteratively removing from the computation those Hypotheses whose estimated p-value has reached a desired level of accuracy, thus investing most of the computational resources on the estimation of the p-values for those hypotheses most likely to be rejected. We present experimental results on simulated and real data and theoretical estimates of the boosting factor we may expect to achieve under several representative circumstances.
Year of Publication2008
KeywordsDifferential analysis, Marker selection, Multiple-hypotheses Testing