Associating microbiome composition with environmental covariates using generalized UniFrac distances.
Associating microbiome composition with environmental covariates using generalized UniFrac distances.
复制标题
使用通用的unifrac距离将微生物组组成与环境协变量相关联。
DOI：
10.1093/bioinformatics/bts342
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发表时间：
2012-08-15
期刊：
影响因子：
--
通讯作者：
Li H
中科院分区：
文献类型：
--
作者：
Chen J;Bittinger K;Charlson ES;Hoffmann C;Lewis J;Wu GD;Collman RG;Bushman FD;Li H
动机：人类微生物组在人类疾病和健康中起着重要作用。鉴定影响微生物组组成的因素可以提供对疾病机制的见解，并提出出于治疗目的调节微生物组组成的方法。基于距离的统计检验已被应用于测试微生物组组成与环境或生物协变量的关联。未加权和加权UniFrac距离是最广泛使用的距离度量。然而，这两个措施分配太多的权重，要么罕见的血统或最丰富的血统，这可能会导致权力的损失时，重要的组成变化发生在适度丰富的血统。结果如下：我们开发了广义UniFrac距离，扩展了加权和未加权的UniFrac距离，用于检测更广泛的生物相关变化。我们使用广泛的Monte Carlo模拟来评估广义UniFrac距离在将微生物组组成与环境协变量相关联中的使用。我们的研究结果表明，使用未加权和加权的UniFrac距离的测试是不太强大的检测丰度的变化在中等丰富的谱系。相比之下，广义UniFrac距离在检测这种变化方面是最强大的，但它几乎保留了检测稀有和高度丰富谱系的所有能力。广义UniFrac距离还具有比联合使用未加权/加权UniFrac距离更好的总体功效。应用于两个真实的微生物组数据集已经证明了在测试人类微生物组与饮食摄入量和习惯性吸烟之间的关联方面的能力。可用性：http://cran.r-project.org/web/packages/GUniFrac联系：hongzhe@upenn.edu补充信息：补充数据可在生物信息学在线。
Motivation: The human microbiome plays an important role in human disease and health. Identification of factors that affect the microbiome composition can provide insights into disease mechanism as well as suggest ways to modulate the microbiome composition for therapeutical purposes. Distance-based statistical tests have been applied to test the association of microbiome composition with environmental or biological covariates. The unweighted and weighted UniFrac distances are the most widely used distance measures. However, these two measures assign too much weight either to rare lineages or to most abundant lineages, which can lead to loss of power when the important composition change occurs in moderately abundant lineages. Results: We develop generalized UniFrac distances that extend the weighted and unweighted UniFrac distances for detecting a much wider range of biologically relevant changes. We evaluate the use of generalized UniFrac distances in associating microbiome composition with environmental covariates using extensive Monte Carlo simulations. Our results show that tests using the unweighted and weighted UniFrac distances are less powerful in detecting abundance change in moderately abundant lineages. In contrast, the generalized UniFrac distance is most powerful in detecting such changes, yet it retains nearly all its power for detecting rare and highly abundant lineages. The generalized UniFrac distance also has an overall better power than the joint use of unweighted/weighted UniFrac distances. Application to two real microbiome datasets has demonstrated gains in power in testing the associations between human microbiome and diet intakes and habitual smoking. Availability: http://cran.r-project.org/web/packages/GUniFrac Contact: hongzhe@upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
64.8
作者：
通讯作者：
--
影响因子：
4.2
作者：
Wu GD;Lewis JD;Hoffmann C;Chen YY;Knight R;Bittinger K;Hwang J;Chen J;Berkowsky R;Nessel L;Li H;Bushman FD
通讯作者：
Bushman FD
DOI：
10.1126/science.1198719
发表时间：
2011-05-20
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Muegge BD;Kuczynski J;Knights D;Clemente JC;González A;Fontana L;Henrissat B;Knight R;Gordon JI
通讯作者：
Gordon JI
影响因子：
64.8
作者：
Arumugam, Manimozhiyan;Raes, Jeroen;Pelletier, Eric;Le Paslier, Denis;Yamada, Takuji;Mende, Daniel R.;Fernandes, Gabriel R.;Tap, Julien;Bruls, Thomas;Batto, Jean-Michel;Bertalan, Marcelo;Borruel, Natalia;Casellas, Francesc;Fernandez, Leyden;Gautier, Laurent;Hansen, Torben;Hattori, Masahira;Hayashi, Tetsuya;Kleerebezem, Michiel;Kurokawa, Ken;Leclerc, Marion;Levenez, Florence;Manichanh, Chaysavanh;Nielsen, H. Bjorn;Nielsen, Trine;Pons, Nicolas;Poulain, Julie;Qin, Junjie;Sicheritz-Ponten, Thomas;Tims, Sebastian;Torrents, David;Ugarte, Edgardo;Zoetendal, Erwin G.;Wang, Jun;Guarner, Francisco;Pedersen, Oluf;de Vos, Willem M.;Brunak, Soren;Dore, Joel;Weissenbach, Jean;Ehrlich, S. Dusko;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
11
作者：
Schloss, Patrick D.
通讯作者：
Schloss, Patrick D.