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

Publication Type:

Journal Article

Source:

Biostatistics (Oxford, England), Volume 9, Issue 1, p.18-29 (2008)

Keywords:

2008, Algorithms, Brain Neoplasms, Breast Neoplasms, Computer Simulation, Data Interpretation, Statistical, Female, Gene Dosage, Genome, Human, Glioblastoma, Humans, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis, Public Health Sciences Division

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.