Toward A Scalable Exploratory Framework for Complex High-Dimensional Phenomics Data
Toward A Scalable Exploratory Framework for Complex High-Dimensional Phenomics Data
复制标题
面向复杂高维表型组数据的可扩展探索框架
DOI：
10.1101/159954
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
P. Schnable
中科院分区：
文献类型：
--
作者：
M. Kamruzzaman;A. Kalyanaraman;B. Krishnamoorthy;P. Schnable
动机表型组学是现代生物学的一个新兴分支，它使用高通量表型工具来大规模捕获多种环境和表型性状测量。由此产生的高维数据集为深入了解多种因素如何相互作用并有助于控制不同植物作物基因型的生长和行为提供了宝贵的信息。然而，目前缺乏能够解析这种高维数据集并帮助提取合理假设的计算工具。在本文中，我们提出了一种新的算法方法，可以从复杂的表型数据中有效地解码和表征环境对表型性状的作用。据我们所知，这项工作代表了拓扑数据分析在表型组学数据上的首次应用。我们将这种新颖的算法应用于真实世界的玉米数据集。我们的研究结果表明，我们的方法能够描述亚种群中由一个或多个环境因素决定的紧急行为；值得注意的是，我们的方法显示了环境如何在决定两种基因型之一的表型行为中起关键作用。可下载的源代码和测试数据可免费获得，指令集在https://xperthut.github.io/HYPPO-X。联系ananth@eecs.wsu.edu补充信息补充数据可在Bioinformatics网站在线获取。
Motivation Phenomics is an emerging branch of modern biology, which uses high throughput phenotyping tools to capture multiple environment and phenotypic trait measurements, at a massive scale. The resulting high dimensional data sets represent a treasure trove of information for providing an indepth understanding of how multiple factors interact and contribute to control the growth and behavior of different plant crop genotypes. However, computational tools that can parse through such high dimensional data sets and aid in extracting plausible hypothesis are currently lacking. In this paper, we present a new algorithmic approach to effectively decode and characterize the role of environment on phenotypic traits, from complex phenomic data. To the best of our knowledge, this effort represents the first application of topological data analysis on phenomics data. Results We applied this novel algorithmic approach on a real-world maize data set. Our results demonstrate the ability of our approach to delineate emergent behavior among subpopulations, as dictated by one or more environmental factors; notably, our approach shows how the environment plays a key role in determining the phenotypic behavior of one of the two genotypes. Availability Downloadable Source code and test data are freely available with instruction set at https://xperthut.github.io/HYPPO-X. contact ananth@eecs.wsu.edu Supplementary Information Supplementary data are available at Bioinformatics online.
影响因子：
9.8
作者：
Lou, Xiang-Yang;Chen, Guo-Bo;Li, Ming D.
通讯作者：
Li, Ming D.