| 董晓强,许树红,陶然,王彤.高维组学数据分析中的贝叶斯变量选择方法[J].Chinese journal of Epidemiology,2017,38(5):679-683 |
| 高维组学数据分析中的贝叶斯变量选择方法 |
| Introduction to Bayesian variable selection methods in high-dimensional omics data analysis |
| Received:November 19, 2016 |
| DOI：10.3760/cma.j.issn.0254-6450.2017.05.025 |
| KeyWord: 高维数据 贝叶斯变量选择 g先验 Non-local先验 |
| English Key Word: High-dimensional data Bayesian variable selection g-prior Non-local prior |
| FundProject:国家自然科学基金（81473073） |
| Author Name | Affiliation | E-mail | | Dong Xiaoqiang | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | | | Xu Shuhong | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | | | Tao Ran | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | | | Wang Tong | Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China | tongwang@sxmu.edu.cn |
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| Abstract: |
| 随着基因组测序技术和生物信息学的迅猛发展，近几年涌现了大量与疾病相关的组学数据即所谓高维数据。对于这类组学数据，共同特点是自变量个数p通常远大于观察例数n，且自变量间往往高度相关，从成千上万个组学数据中识别出真正有意义的自变量带来一些统计学挑战。本文对高维数据中的贝叶斯变量选择方法做论述。 |
| English Abstract: |
| With the rapid development of genome sequencing technology and bioinformatics in recent years, it has become possible to measure thousands of omics data which might be associated with the progress of diseases, i.e. “high-dimensional data”. This type of omics data have a common feature that the number of variable p is usually greater than the observation cases n, and often has high correlation between independent variables. Therefore, it is a great statistical challenge to identify really meaningful variables from omics data. This paper summarizes the methods of Bayesian variable selection in the analysis of high-dimensional data. |
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