A latent allocation model for the analysis of microbial composition and disease
A latent allocation model for the analysis of microbial composition and disease
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DOI：
10.1186/s12859-018-2530-6
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发表时间：
2018-12-31
影响因子：
3
通讯作者：
Shimamura, Teppei
中科院分区：
文献类型：
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作者：
Abe, Ko;Hirayama, Masaaki;Shimamura, Teppei
建立微生物群和特定疾病之间的关系很重要，但需要适当的统计方法。微生物组计数数据的一个特殊特征是存在大量的零，这使得在病例对照研究中难以分析。大多数现有的方法要么添加一个称为伪计数的小数字，要么使用概率模型，如多项式和狄利克雷多项式分布来解释多余的零计数，这可能会产生不必要的偏差，并施加相关结构，这是不适合微生物组data.ResultsThe本文的目的是开发一种新的概率模型，称为Bernoulli和基于多项分布的潜在分配（BERMUDA），来解决这些问题。BERMUDA使我们能够描述样品中细菌组成和某种疾病的差异。我们还提供了一个简单而有效的学习过程中所提出的模型使用退火EM algorithm.ConclusionWe说明了所提出的方法的性能，通过模拟和真实的数据分析。BERMUDA是用R实现的，可以从GitHub（https：//github.com/abikoushi/Bermuda）获得。
BackgroundEstablishing the relationship between microbiota and specific diseases is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data.ResultsThe purpose of this article is to develop a new probabilistic model, called BERnoulli and MUltinomial Distribution-based latent Allocation (BERMUDA), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm.ConclusionWe illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub (https://github.com/abikoushi/Bermuda).