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微生物组学中的高维计数和成分数据分析
High-dimensional count and compositional data analysis in microbiome studies
【摘要】 人体微生物组对人体健康和疾病起着重要作用.高通量测序技术的发展使得我们可以定量分析微生物组中所有菌种的成分.本文回顾近来在微生物组学研究中的高维计数和成分数据分析方法,其中包括Dirichlet多项分布模型及其拓展、从大维稀疏计数矩阵估计成分数据、高维成分回归和基于对数基底的成分数据统计推断方法.
【Abstract】 The human microbiome plays an important role in human health and disease. The development of high-throughput sequencing technologies makes it possible to quantify all microbes constituting the microbiome.In this paper, we give a review of recent advances in high-dimensional count and compositional data analysis in microbiome studies. It includes the Dirichlet-multinomial model and its extensions, composition estimation from large sparse count matrix, high-dimensional regression with compositional covariates, and statistical inference for log-basis-based compositional data.
【Key words】 high-dimensional count; compositional data; Dirichlet-multinomial model; sparsity; identifiability; regression model;
- 【文献出处】 中国科学:数学 ,Scientia Sinica(Mathematica) , 编辑部邮箱 ,2017年12期
- 【分类号】O212;Q933
- 【被引频次】8
- 【下载频次】304