对微生物组数据的逻辑正常多项式回归模型的混合物.
Wenshu Dai1, Yuan Fang2, Sanjeena Subedi3
1Department of Mathematics and Statistics, Binghamton University, Binghamton, NY, USA.
Journal of applied statistics
|February 14, 2025
概括
这项研究引入了一种新的方法来聚类微生物组数据,将其视为组成数据. 该方法有助于分析与饮食和年龄等因素相关的细菌丰度.
科学领域:
- 生物信息学是一种生物信息学.
- 微生物组的分析
- 统计建模 统计建模
背景情况:
- 来自16S rRNA测序的微生物群种数量数据是高维和组成的.
- 分析这些数据是具有挑战性的,因为其简单的限制和共变量的影响.
- 现有的方法可能无法完全捕捉微生物组数据关系的复杂性.
研究的目的:
- 开发一种基于回归的新型混合模型,用于集群微生物组数据.
- 为了能够探索细菌丰富度和在已识别的集群内的共变体之间的关系.
- 提高微生物组数据分析的参数估计的准确性和效率.
主要方法:
- 开发了基于回归的物流正常多项式模型的混合物.
- 使用变量高斯近似 (VGA) 进行参数估计.
- 将该方法应用于模拟和真实微生物组数据集.
主要成果:
- 提出的模型有效地将样本分类为同质的子群体.
- 证明了在已识别的细菌群体内探索共同变量关系的能力.
- VGA框架提高了参数估计的准确性和效率.
结论:
- 新型集群方法对微生物组数据分析有效.
- 该方法有助于理解对细菌丰富度的生物和环境影响.
- 这种方法为生物信息学中的组成数据分析提供了一个强大的框架.
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
25
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
25
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38