在组成数据回归中贝叶斯变量收缩和选择:对口腔微生物群的应用
Jyotishka Datta1, Dipankar Bandyopadhyay2
1Department of Statistics, Virginia Polytechnic Institute and State University, 250 Drillfield Drive, Blacksburg, VA 24061 USA.
概括
这项研究引入了贝叶斯的方法,使用连续收缩先验进行微生物组数据分析. 这些方法有效地确定组合数据中协变量和分类学丰度之间的显著关联.
科学领域:
- 微生物学 微生物学
- 统计建模 统计建模
- 生物信息学是一种生物信息学.
背景情况:
- 微生物组研究产生复杂的组成数据 (例如,种群数量),这些数据是非负的,有界的,和和为一个.
- 迪里克莱多项式回归 (D-M) 是用共变量建模此类数据的常用框架.
- 有效的变量选择对于识别高维微生物组数据中的关键关联至关重要.
研究的目的:
- 介绍一个贝叶斯的方法,用于迪里克莱特多项式组合数据分析.
- 为了比较连续收缩先验 (马,马+) 与贝叶斯拉索对变量选择的性能.
- 在微生物组数据中确定共变量和分类学丰度之间的显著关联.
主要方法:
- 在D-M回归框架下贝叶斯估计和推断.
- 马,马+和贝叶斯拉索先验的比较评估.
- 利用哈密尔顿式蒙特卡洛来进行后续采样,并生成可信的间隔.
- 应用于模拟研究和真实口腔微生物组数据的合成数据的方法 (NYC-Hanes研究).
主要成果:
- 连续收缩先验在稀疏的参数设置中显示出出色的恢复和估计准确性.
- 马和马+先验在识别显著的共变量-税率关联方面表现强.
- 这种方法在口腔微生物组数据上得到了成功的说明.
结论:
- 贝叶斯连续收缩先验为D-M组合微生物组数据中的变量选择提供了一个有效的策略.
- 提出的方法提高了识别微生物群落组成的关键驱动因素的能力.
- 一个RStan实现可用于更广泛的应用.
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