Spatial smoothing and hot spot detection for CGH data using the fused lasso. (English) Zbl 1274.62886
Summary: We apply the “fused lasso” regression method 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.
MSC:
62P10 | Applications of statistics to biology and medical sciences; meta analysis |
62J07 | Ridge regression; shrinkage estimators (Lasso) |
65C60 | Computational problems in statistics (MSC2010) |