Spatial smoothing and hot spot detection for CGH data using the fused lasso

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.