Bayesian Sparse Dirichlet-Multinomial Models for Discovering Latent Structure in High-Dimensional Compositional Count Data

用于发现高维组合计数数据中潜在结构的贝叶斯稀疏狄利克雷多项模型

• 批准号: 2245492
• 负责人: Matthew Koslovsky
• 金额: $ 16.5万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245492&HistoricalAwards=false
• 关键词: Bayesian; Sparse; Dirichlet; Multinomial; Models

The collection and analysis of microbiome data have broad implications for furthering our understanding of human health and performance, agriculture, and ecology, among other areas. Human microbiome research, for example, aims to better understand the role of our microbial communities and how they interact with their host, respond to their environment, and influence disease. In addition to microbiome data being compositional, as the sum of the microbial taxa reads is fixed, and high-dimensional, they are also zero-inflated, as there are typically more zero reads observed than expected, which has profound implications on modeling and inference. This project aims to advance statistical methods and computational algorithms for the analysis of zero-inflated multivariate compositional count data. While developed to address the current challenges of microbiome data analysis, the methods will be generally applicable to other settings in which multivariate compositional count data with excess zeros are observed, including biomedical and public health research, econometrics, and ecology. The project will additionally provide educational and professional training and mentoring to graduate students.Analyzing multivariate count data generated by high-throughput sequencing technology in omics research is challenging due to the high-dimensional and compositional structure of the data, over-dispersion, and potential zero inflation. In practice, researchers often use the Dirichlet-multinomial (DM) distribution and its variants to model these data. However, under the assumptions of a DM model, estimated probabilities for zero counts are strictly positive even if the true probability of occurrence is zero. This research project aims to develop a novel sparse DM (sDM) model which allows zero count probabilities to take on zero values to simultaneously accommodate potential zero inflation in multivariate compositional count data while estimating compositional probabilities. Additionally, this project will investigate extensions of the sDM modeling framework to high-dimensional variable selection and clustering problems and contribute Markov chain Monte Carlo algorithms for posterior inference that will be made publicly available to practitioners and other researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

微生物组数据的收集和分析对我们对人类健康和绩效，农业和生态学等领域以及其他领域的了解具有广泛的影响。例如，人类微生物组的研究旨在更好地了解我们微生物群落的作用，以及它们如何与宿主相互作用，对环境反应并影响疾病。除了微生物组数据的组成之外，由于微生物分类单元读取的总和是固定的，而且高维的，它们也被泄漏到零，因为通常观察到的零读数比预期的要多，这对建模和推断具有深远的影响。该项目旨在推进统计方法和计算算法，以分析零膨胀的多元组成计数数据。虽然开发了用于应对微生物组数据分析的当前挑战，但这些方法通常适用于其他观察到具有过量零的多元组成计数数据，包括生物医学和公共卫生研究，计量经济学和生态学。该项目还将为研究生提供教育和专业的培训和指导。由于数据，过度分散和潜在的零通货膨胀的高维和组成结构的高维和组成结构，因此在OMICS研究中由高通量测序技术产生的多变量计数数据具有挑战性。在实践中，研究人员经常使用Dirichlet-Multinomial（DM）分布及其变体来对这些数据进行建模。但是，在DM模型的假设下，即使出现的真实概率为零，零计数的估计概率也是严格的。该研究项目旨在开发一种新型的稀疏DM（SDM）模型，该模型允许零计数概率以零值为零，以同时适应多变量组成计数数据中的潜在零通胀，同时估计组成概率。此外，该项目将调查SDM建模框架的扩展，以对高维变量选择和聚类问题进行贡献，并为马尔可夫链蒙特卡洛算法提供后推理，该算法将公开可为从业人员和其他研究人员公开可用。该奖项反映了NSF的法定任务，反映了通过评估的构成群体的构成群体的范围。