Tibshirani Robert, Wang Pei
Department of Health, Stanford University Stanford, CA 94305, USA.
Biostatistics. 2008 Jan;9(1):18-29. doi: 10.1093/biostatistics/kxm013. Epub 2007 May 18.
We apply the "fused lasso" regression method of (TSRZ2004) to the problem of "hot- spot detection", in particular, detection of regions of gain or loss in comparative genomic hybridization (CGH) data. The fused lasso criterion leads to a convex optimization problem, and we provide a fast algorithm for its solution. Estimates of false-discovery rate are also provided. Our studies show that the new method generally outperforms competing methods for calling gains and losses in CGH data.
我们将(TSRZ2004)的“融合套索”回归方法应用于“热点检测”问题，特别是在比较基因组杂交(CGH)数据中检测增益或缺失区域。融合套索准则导致一个凸优化问题，我们提供了一种快速求解算法。还提供了错误发现率的估计。我们的研究表明，新方法在调用CGH数据中的增益和缺失方面通常优于其他竞争方法。
