title: Spatial smoothing and hot spot detection for CGH data using the fused lasso.
abstract: 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.
authors.affiliation: Department of Health, Stanford University Stanford, CA 94305, USA. tibs@stat.stanford.edu
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D018628:Gene Dosage; D015894:Genome, Human; D005909:Glioblastoma / Q000235:genetics; D006801:Humans; D009693:Nucleic Acid Hybridization / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis