Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data
Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data
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DOI：
10.3389/fgene.2019.00516
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发表时间：
2019-06-06
影响因子：
3.7
通讯作者：
Mueller, Christian L.
中科院分区：
文献类型：
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作者：
Yoon, Grace;Gaynanova, Irina;Mueller, Christian L.
高通量微生物测序技术，例如基于扩增子和宏基因组分析，提供了自然环境中微生物群落的低成本基因组调查数据，从海洋生态系统到宿主相关的栖息地。虽然标准的微生物组分析数据可以提供操作分类单元或基因的稀疏相对丰度，但实验方案的最新进展通过将基于测序的技术与来自同一样品的微生物细胞计数的正交测量结果配对，从而提供了微生物群落的更定量图像。这些串联测量值提供了绝对的微生物计数数据，尽管由于测序深度有限，但零过量的零过量。在此贡献中，我们考虑了从这种定量微生物组数据中估计相关性和部分相关性的基本统计问题。为此，我们提出了一种基于半参数等级的相关估计方法，该方法自然可以处理数据中的多余零。将该估计器与稀疏图形建模技术相结合，导致基于半参数级别的图形模型（Spring）推断。 Spring可以从定量微生物组数据中推断统计微生物关联网络，这些网络可以用作基础微生物生态系统的高级统计摘要，并可以为功能性物种相互作用提供可检验的假设。由于没有经过验证的微生物关联，我们还引入了一种新型的定量微生物组数据生成机制，该机制模仿了测量计数数据的经验边缘分布，同时允许变量之间的用户指定的依赖关系。春季在各种现实的基准问题上显示出卓越的网络恢复性能，其网络拓扑不同，并且对总细胞数量估计值的误解是可靠的。为了突出春季的广泛适用性，我们从美国肠道项目数据和属属的关联中推断出分类单元 - 塔克森关联，从最近的定量肠道微生物组数据集中推断出。我们认为，随着定量微生物组分析数据将变得越来越多，此处引入的相关性和部分相关估计的半参数估计器为可靠的定量微生物组数据提供了可靠的统计分析。
High-throughput microbial sequencing techniques, such as targeted amplicon-based and metagenomic profiling, provide low-cost genomic survey data of microbial communities in their natural environment, ranging from marine ecosystems to host-associated habitats. While standard microbiome profiling data can provide sparse relative abundances of operational taxonomic units or genes, recent advances in experimental protocols give a more quantitative picture of microbial communities by pairing sequencing-based techniques with orthogonal measurements of microbial cell counts from the same sample. These tandem measurements provide absolute microbial count data albeit with a large excess of zeros due to limited sequencing depth. In this contribution we consider the fundamental statistical problem of estimating correlations and partial correlations from such quantitative microbiome data. To this end, we propose a semi-parametric rank-based approach to correlation estimation that can naturally deal with the excess zeros in the data. Combining this estimator with sparse graphical modeling techniques leads to the Semi-Parametric Rank-based approach for INference in Graphical model (SPRING). SPRING enables inference of statistical microbial association networks from quantitative microbiome data which can serve as high-level statistical summary of the underlying microbial ecosystem and can provide testable hypotheses for functional species-species interactions. Due to the absence of verified microbial associations we also introduce a novel quantitative microbiome data generation mechanism which mimics empirical marginal distributions of measured count data while simultaneously allowing user-specified dependencies among the variables. SPRING shows superior network recovery performance on a wide range of realistic benchmark problems with varying network topologies and is robust to misspecifications of the total cell count estimate. To highlight SPRING'S broad applicability we infer taxon-taxon associations from the American Gut Project data and genus-genus associations from a recent quantitative gut microbiome dataset. We believe that, as quantitative microbiome profiling data will become increasingly available, the semi-parametric estimators for correlation and partial correlation estimation introduced here provide an important tool for reliable statistical analysis of quantitative microbiome data.