A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data.
A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data.
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作者：
Liu J;Zhang X;Chen T;Wu T;Lin T;Jiang L;Lang S;Liu L;Natarajan L;Tu JX;Kosciolek T;Morton J;Nguyen TT;Schnabl B;Knight R;Feng C;Zhong Y;Tu XM
人类微生物组在我们的健康中起着重要作用，识别与微生物组组成相关的因素可以深入了解固有的疾病机制。通过在高通量测序中对标记基因进行扩增和测序，将高度相似的序列合并在一起，我们获得了每个受试者的操作分类单位（OTU）概况。由于OTU的高维性和非正态性特征，引入多样性度量作为微生物群落水平的概括，包括个体之间基于距离的β多样性。此类受试者间属性的分析不适用于主要的基于受试者内的统计范式，如t检验和线性回归。在本文中，我们提出了一种新的方法来模拟β多样性作为一个回归设置内的响应，通过利用功能反应模型（FRM），一类半参数模型之间，以及内的主题属性。这种新方法不仅解决了当前β多样性方法的局限性，而且为将来将这种方法扩展到纵向和其他聚类数据提供了前提。所提出的方法说明与真实的和模拟数据。
The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain Operational Taxonomic Units (OTU) profiles for each subject. Due to the high-dimensionality and non-normality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based Beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model Beta-diversity as a response within a regression setting by utilizing the functional response models (FRM), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for Beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.
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DOI：
10.1084/jem.20180448
发表时间：
2019-01-07
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Durack J;Lynch SV
通讯作者：
Lynch SV
DOI：
10.1111/rssb.12238
发表时间：
2018-01-01
影响因子：
5.8
作者：
Hemerik, Jesse;Goeman, Jelle J.
通讯作者：
Goeman, Jelle J.
影响因子：
13.5
作者：
Lang, Sonja;Duan, Yi;Schnabl, Bernd
通讯作者：
Schnabl, Bernd
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
4.4
作者：
Lozupone, C;Knight, R
通讯作者：
Knight, R