Hierarchical Bayesian analysis of somatic mutation data in cancer
View/ Open
Author
Ding, Jie
Trippa, Lorenzo
Zhong, Xiaogang
Parmigiani, Giovanni
Published Version
https://doi.org/10.1214/12-AOAS604Metadata
Show full item recordCitation
Ding, Jie, Lorenzo Trippa, Xiaogang Zhong, and Giovanni Parmigiani. 2013. “Hierarchical Bayesian Analysis of Somatic Mutation Data in Cancer.” The Annals of Applied Statistics 7 (2): 883–903. https://doi.org/10.1214/12-aoas604.Abstract
Identifying genes underlying cancer development is critical to cancer biology and has important implications across prevention, diagnosis and treatment. Cancer sequencing studies aim at discovering genes with high frequencies of somatic mutations in specific types of cancer, as these genes are potential driving factors (drivers) for cancer development. We introduce a hierarchical Bayesian methodology to estimate gene-specific mutation rates and driver probabilities from somatic mutation data and to shed light on the overall proportion of drivers among sequenced genes. Our methodology applies to different experimental designs used in practice, including one-stage, two-stage and candidate gene designs. Also, sample sizes are typically small relative to the rarity of individual mutations. Via a shrinkage method borrowing strength from the whole genome in assessing individual genes, we reinforce inference and address the selection effects induced by multistage designs. Our simulation studies show that the posterior driver probabilities provide a nearly unbiased false discovery rate estimate. We apply our methods to pancreatic and breast cancer data, contrast our results to previous estimates and provide estimated proportions of drivers for these two types of cancer.Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:41552041
Collections
- SPH Scholarly Articles [6266]
Contact administrator regarding this item (to report mistakes or request changes)