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	CCLasso: correlation inference for compositional data through Lasso
	Fang, Huaying ; Huang, Chengcheng ; Zhao, Hongyu ; Deng, Minghua
	2015
关键词	HUMAN MICROBIOME SELECTION
英文摘要	Motivation: Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study. Traditional Pearson correlation analysis treating the observed data as absolute abundances of the microbes may lead to spurious results because the data only represent relative abundances. Special care and appropriate methods are required prior to correlation analysis for these compositional data. Results: In this article, we first discuss the correlation definition of latent variables for compositional data. We then propose a novel method called CCLasso based on least squares with l(1) penalty to infer the correlation network for latent variables of compositional data from metagenomic data. An effective alternating direction algorithm from augmented Lagrangian method is used to solve the optimization problem. The simulation results show that CCLasso outperforms existing methods, e.g. SparCC, in edge recovery for compositional data. It also compares well with SparCC in estimating correlation network of microbe species from the Human Microbiome Project.; National Natural Science Foundation of China [31171262, 31428012, 31471246]; National Key Basic Research Project of China [2015CB910303]; Graduate School of Peking University; NIH [GM59507]; SCI(E); PubMed; ARTICLE; dengmh@pku.edu.cn; 19; 3172-3180; 31
语种	英语
出处	PubMed ; SCI
出版者	BIOINFORMATICS
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/415896]
专题	数学科学学院
推荐引用方式 GB/T 7714	Fang, Huaying,Huang, Chengcheng,Zhao, Hongyu,et al. CCLasso: correlation inference for compositional data through Lasso. 2015-01-01.

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