Path-level interpretation of Gaussian graphical models using the pair-path subscore.
Path-level interpretation of Gaussian graphical models using the pair-path subscore.
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DOI：
10.1186/s12859-021-04542-5
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发表时间：
2022-01-05
影响因子：
3
通讯作者：
Scholtens DM
中科院分区：
文献类型：
--
作者：
Gill NP;Balasubramanian R;Bain JR;Muehlbauer MJ;Lowe WL Jr;Scholtens DM
从横截面生物数据构建网络越来越普遍。最近的许多方法都是基于高斯图形建模，并优先估计网络中节点之间的条件成对依赖关系。然而，挑战仍然存在于如何特定的路径，通过由此产生的网络有助于整体的“网络级”的相关性。对于生物学应用，理解这些关系对于解析包含在复杂子网络中的结构信息特别相关。我们提出了对路径子分数（PPS），一种在单个网络路径水平上解释高斯图形模型的方法。评分是基于这些路径在确定它们的终端节点之间的皮尔逊相关性中的相对重要性。PPS使用来自Hyperlipidemic和不良妊娠结局（HAPO）研究的人体代谢组学数据进行验证，观察结果证实了代谢物之间的生物学关系。我们还强调了PPS如何以探索性的方式用于产生新的生物学假设。我们的方法在R包pps中实现，可在https://github.com/nathan-gill/pps上获得。PPS可用于通过调查潜在复杂拓扑中的哪些路径对边际行为的贡献最大来在更精细的尺度上探测网络结构。将PPS添加到网络分析工具包中可以使研究人员能够提出有关网络数据中节点之间关系的新问题。在线版本包含补充材料，可通过10.1186/s12859-021-04542-5获得。
Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall ‘network-level’ correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps. The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data. The online version contains supplementary material available at 10.1186/s12859-021-04542-5.
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