MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities
MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities
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DOI：
10.1371/journal.pone.0160169
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发表时间：
2016-08-11
期刊：
影响因子：
3.7
通讯作者：
Rondeau, Pascale
中科院分区：
文献类型：
--
作者：
Cao, Kim-Anh Le;Costello, Mary-Ellen;Rondeau, Pascale
不依赖培养的技术，例如鸟枪宏基因组学和16 S rRNA扩增子测序，极大地改变了我们检查微生物群落的方式。最近，微生物群落结构和动力学的变化与越来越多的人类疾病有关。识别和比较驱动这些变化的细菌需要开发合理的统计工具，特别是如果微生物生物标志物将用于临床环境。我们提出了mixMC，一种新的多变量数据分析框架的宏基因组生物标志物发现。mixMC解释了16S数据的组成性质，并且当由于在多个生境中对相同受试者重复进行微生物采样而存在高受试者间变异性时，能够检测到细微差异。通过数据降维，多变量方法提供了有见地的图形可视化，以详细的方式描述每种类型的环境。我们将mixMC应用于16S微生物组研究，重点关注健康个体的多个身体部位，将我们的结果与现有的统计工具进行了比较，并说明了使用多变量方法来充分验证和比较微生物群落的附加值。
Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.