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

We apply the "fused lasso" regression method of Tibshirani and others (2004) to the problem of "hotspot 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.