Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis
Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis
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DOI：
10.1093/biostatistics/kxs038
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发表时间：
2013-04-01
期刊：
影响因子：
2.1
通讯作者：
Li, Hongzhe
中科院分区：
文献类型：
--
作者：
Chen, Jun;Bushman, Frederic D.;Li, Hongzhe
在研究营养摄入与人类肠道微生物组组成之间关系的基础上，我们开发了一种高维环境下结构约束稀疏典型相关分析（ssCCA）方法。ssCCA考虑了细菌之间的系统发育关系，为细菌分类群之间的进化关系提供了重要的先验知识。我们的ssCCA公式利用系统发育结构约束的惩罚函数，根据分类群之间的系统发育关系对线性系数施加一定的平滑性。提出了一种高效的坐标下降算法。人类肠道微生物组数据集被用来说明这种方法。仿真和实际数据应用表明，当数据中存在结构时，ssCCA在识别有意义变量方面优于标准稀疏CCA。
Motivated by studying the association between nutrient intake and human gut microbiome composition, we developed a method for structure-constrained sparse canonical correlation analysis (ssCCA) in a high-dimensional setting. ssCCA takes into account the phylogenetic relationships among bacteria, which provides important prior knowledge on evolutionary relationships among bacterial taxa. Our ssCCA formulation utilizes a phylogenetic structure-constrained penalty function to impose certain smoothness on the linear coefficients according to the phylogenetic relationships among the taxa. An efficient coordinate descent algorithm is developed for optimization. A human gut microbiome data set is used to illustrate this method. Both simulations and real data applications show that ssCCA performs better than the standard sparse CCA in identifying meaningful variables when there are structures in the data.