'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets
'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets
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DOI：
10.1038/s41396-018-0291-x
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发表时间：
2019-02-01
期刊：
影响因子：
11
通讯作者：
Mande, Sharmila S.
中科院分区：
文献类型：
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作者：
Kuntal, Bhusan K.;Chandrakar, Pranjal;Mande, Sharmila S.
已知共同居住在人体内的微生物之间的相互关联的组合效应在确定个体的健康状态中起主要作用。健康和疾病之间的差异分类丰度通常用于鉴定微生物标记。然而，为了进行基于微生物群落的推断，重要的是不仅要考虑微生物丰度，而且要量化在微生物间协会中观察到的变化。在本研究中，我们引入了一种称为“NetShift”的方法来量化健康和疾病之间微生物关联网络的重新布线和社区变化。此外，我们设计了一个分数来识别重要的微生物类群，这些类群作为从健康到疾病的“驱动程序”。我们证明了我们的分数在一些情况下的有效性，并将我们的方法应用于两个真实的世界宏基因组数据集。
The combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify microbial markers. However, in order to make a microbial community based inference, it is important not only to consider microbial abundances, but also to quantify the changes observed among inter microbial associations. In the present study, we introduce a method called 'NetShift' to quantify rewiring and community changes in microbial association networks between healthy and disease. Additionally, we devise a score to identify important microbial taxa which serve as 'drivers' from the healthy to disease. We demonstrate the validity of our score on a number of scenarios and apply our methodology on two real world metagenomic datasets.