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	gCoda: Conditional Dependence Network Inference for Compositional Data
	Fang, Huaying ; Huang, Chengcheng ; Zhao, Hongyu ; Deng, Minghua
	2017
关键词	compositional data direct interaction inverse covariance matrix microbial network latent variable model majorization-minimization algorithm HUMAN MICROBIOME LASSO SELECTION MODELS
英文摘要	The increasing quality and the reducing cost of high-throughput sequencing technologies for 16S rRNA gene profiling enable researchers to directly analyze microbe communities in natural environments. The direct interactions among microbial species of a given ecological system can help us understand the principles of community assembly and maintenance under various conditions. Compositionality and dimensionality of microbiome data are two main challenges for inferring the direct interaction network of microbes. In this article, we use the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data. The direct interaction relationships are then modeled via the conditional dependence network under this logistic normal assumption. We then propose a novel penalized maximum likelihood method called gCoda to estimate the sparse structure of inverse covariance for latent normal variables to address the high dimensionality of the microbiome data. An effective Majorization-Minimization algorithm is proposed to solve the optimization problem in gCoda. Simulation studies show that gCoda outperforms existing methods (e.g., SPIEC-EASI) in edge recovery of inverse covariance for compositional data under a variety of scenarios. gCoda also performs better than SPIEC-EASI for inferring direct microbial interactions of mouse skin microbiome data.; National Key Basic Research Project of China [2015CB910303]; National Key Research and Development Program of China [2016YFA0502300]; National Natural Science Foundation of China [31171262, 31428012, 31471246]; Graduate School of Peking University; NIH [GM59507]; SCI(E); ARTICLE; 7; 699-708; 24
语种	英语
出处	SCI
出版者	JOURNAL OF COMPUTATIONAL BIOLOGY
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/472469]
专题	数学科学学院 生命科学学院
推荐引用方式 GB/T 7714	Fang, Huaying,Huang, Chengcheng,Zhao, Hongyu,et al. gCoda: Conditional Dependence Network Inference for Compositional Data. 2017-01-01.

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