Detecting Divergent Subpopulations in Phenomics Data using Interesting Flares
Detecting Divergent Subpopulations in Phenomics Data using Interesting Flares
复制标题
使用有趣的耀斑检测表型组数据中的不同亚群
DOI：
10.1145/3233547.3233593
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Krishnamoorthy, Bala
中科院分区：
文献类型：
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作者：
Kamruzzaman, Methun;Kalyanaraman, Ananth;Krishnamoorthy, Bala
现代生物学的重大挑战之一是了解基因型(G)和环境(E)如何相互作用以影响表型(P)，即G × E - P。表型组学是一个新兴领域，旨在研究包含基因型、环境、表型读数组合的大型复杂数据集。在这方面，一个非常有趣的现象是不同的亚种群，即种群的某些亚种群在不同类型的环境条件下如何表现出不同的行为。我们认为通过分析来自大量不同种群的高维表型组学数据集来识别这种“有趣的”亚种群水平行为的基本任务。然而，由于表型组学数据的大尺寸、高维度和复杂性，描绘这样的亚种群是一项具有挑战性的任务。我们提出了一个从表型组学数据中提取亚种群水平信息的新框架。我们的方法是基于代数拓扑的原理，代数拓扑是数学的一个分支，以稳健的方式研究数据的形状和结构。特别是，我们的框架以无监督的方式识别和量化“耀斑”，这是数据中的结构分支特征，表征了亚种群的不同行为。我们提出了检测和排列耀斑的算法，并在两个现实世界的植物表型组学数据集上展示了所提出的框架的实用性。
One of the grand challenges of modern biology is to understand how genotypes (G) and environments (E) interact to affect phenotypes (P), i.e., G × E - P . Phenomics is the emerging field that aims to study large and complex data sets encompassing combinations of genotypes, environments, phenotypes readings. A phenomenon of crucial interest in this context is that of divergent subpopulations, i.e., how certain subgroups of the population show differential behavior under different types of environmental conditions. We consider the fundamental task of identifying such "interesting" subpopulation-level behavior by analyzing high-dimensional phenomics data sets from a large and diverse population. However, delineation of such subpopulations is a challenging task due to the large size, high dimensionality, and complexity of phenomics data. We present a new framework to extract such subpopulation-level information from phenomics data. Our approach is based on principles from algebraic topology, a branch of mathematics that studies shapes and structure of data in a robust manner. In particular, our framework identifies and quantifies "flares", which are structural branching features in data that characterize divergent behavior of subpopulations, in an unsupervised manner. We present algorithms to detect and rank flares, and demonstrate the utility of the proposed framework on two real-world plant phenomics data sets.
映射器中有趣的路径
DOI：
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发表时间：
2018
期刊：
ArXiv.org
影响因子：
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作者：
Ananth Kalyanaraman, Methun Kamruzzaman
通讯作者：
Ananth Kalyanaraman, Methun Kamruzzaman
面向复杂高维表型组数据的可扩展探索框架
DOI：
10.1101/159954
发表时间：
2017
期刊：
bioRxiv
影响因子：
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作者：
M. Kamruzzaman;A. Kalyanaraman;B. Krishnamoorthy;P. Schnable
通讯作者：
P. Schnable