论文标题
博士学位论文从微生物数据中推断出多个网络
PhD dissertation to infer multiple networks from microbial data
论文作者
论文摘要
微生物社区的组成成员之间的互动在确定社区的整体行为及其成员的丰富水平方面起着重要作用。可以使用网络对这些相互作用进行建模，该网络的节点代表微生物类群，边缘代表成对的相互作用。微生物网络是一个加权图，它是由样品 - 签名计数矩阵构建的，可用于对微生物群落组成成员的共发生和/或相互作用进行建模。该图中的节点代表微生物类群，边缘代表这些分类单元之间的成对关联。微生物网络通常是由样品键盘计数矩阵构建的，该基质是通过对多个生物样品进行测序并识别分类群数来获得的。从大规模的微生物组研究中，很明显，微生物群落组成和相互作用受环境和/或宿主因素的影响。因此，期望作为大型研究的一部分产生的样品键taxa矩阵涉及多个环境或临床参数可以与多个微生物网络相关联。但是，据我们所知，迄今为止提出的微生物网络推理方法假设样品键taxa矩阵与单个网络相关联。
The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is a weighted graph that is constructed from a sample-taxa count matrix, and can be used to model co-occurrences and/or interactions of the constituent members of a microbial community. The nodes in this graph represent microbial taxa and the edges represent pairwise associations amongst these taxa. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network.
