A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data
A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data
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DOI：
10.1007/s00285-011-0428-2
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发表时间：
2012-03-01
影响因子：
1.9
通讯作者：
Dushoff, Jonathan
中科院分区：
文献类型：
--
作者：
Jiang, Xingpeng;Weitz, Joshua S.;Dushoff, Jonathan
宏基因组研究直接从环境样本中测序DNA，以探索复杂微生物和病毒群落的结构和功能。单个的短片段测序DNA（“reads”）被分类到（假定的）分类或代谢组中，以分析样本中的模式。对这些读取矩阵的分析是利用宏基因组数据推断生态系统结构和功能的核心。非负矩阵分解（NMF）是一种将高维数据点近似为正分量的正线性组合的数值技术。因此，它非常适合于将观察到的样品解释为不同组分的组合。我们开发，测试和应用基于nmf的框架来分析宏基因组读取矩阵。特别地，我们介绍了一种在存在重叠的情况下选择NMF度的方法，并将光谱重排序技术应用于基于NMF的相似性矩阵以帮助可视化。我们的方法可以鲁棒地识别适当的程度，并使用合成数据集分离重叠贡献。然后，我们检查并讨论了从39个公开可用的宏基因组样本中提取的代谢谱矩阵的NMF分解，并确定了典型的样本类型，包括与珊瑚生态系统相关的样本类型，与高盐生态系统相关的样本类型等。我们还确定了路径和规范环境之间的特定关联，并探索了分解的替代选择如何促进在更精细的尺度上分析读取矩阵。
Metagenomic studies sequence DNA directly from environmental samples to explore the structure and function of complex microbial and viral communities. Individual, short pieces of sequenced DNA ("reads") are classified into (putative) taxonomic or metabolic groups which are analyzed for patterns across samples. Analysis of such read matrices is at the core of using metagenomic data to make inferences about ecosystem structure and function. Non-negative matrix factorization (NMF) is a numerical technique for approximating high-dimensional data points as positive linear combinations of positive components. It is thus well suited to interpretation of observed samples as combinations of different components. We develop, test and apply an NMF-based framework to analyze metagenomic read matrices. In particular, we introduce a method for choosing NMF degree in the presence of overlap, and apply spectral-reordering techniques to NMF-based similarity matrices to aid visualization. We show that our method can robustly identify the appropriate degree and disentangle overlapping contributions using synthetic data sets. We then examine and discuss the NMF decomposition of a metabolic profile matrix extracted from 39 publicly available metagenomic samples, and identify canonical sample types, including one associated with coral ecosystems, one associated with highly saline ecosystems and others. We also identify specific associations between pathways and canonical environments, and explore how alternative choices of decompositions facilitate analysis of read matrices at a finer scale.