Collaborative Research: ABI Innovation: A Scalable Framework for Visual Exploration and Hypotheses Extraction of Phenomics Data using Topological Analytics

合作研究：ABI 创新：使用拓扑分析进行表型组数据的可视化探索和假设提取的可扩展框架

• 批准号: 1661348
• 负责人: Anantharaman Kalyanaraman
• 金额: $ 76.14万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661348&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Scalable

Understanding how gene by environment interactions result in specific phenotypes is a core goal of modern biology and has real-world impacts on such things as crop management. Developing and managing successful crop practices is a goal that is fundamentally tied to our national food security. By applying novel computational visual analytical methods, this project seeks to identify and unravel the complex web of interactions linking genotypes, environments and phenotypes. These methods will first need to be designed and developed into usable software applications that can handle large volumes of crop phenomics data. High-throughput sensing technologies collect large volumes of field data for many plant traits, such as flowering time, related to crop development and production. The maize cultivars used here come from multiple genotypes that have been grown under a variety of environmental conditions, in order to give the widest range of conditions for understanding the interactions. The resulting data sets are growing quickly, both in size and complexity, but the analytical tools needed to extract knowledge and catalyze scientific discoveries have significantly lagged behind. The methodologies to be developed in this project represent a systematic attempt at bridging this rapidly widening divide. The project is inherently interdisciplinary, involving close research partnerships among computer scientists, plant scientists, and mathematicians. The research outcomes will be tightly integrated with education using a multipronged approach that includes, among others, postdoctoral and student training (graduates and undergraduates), curriculum development for a new campus-wide interdisciplinary undergraduate degree in Data Analytics, conference tutorials for training phenomics data practitioners, and contribution to the recruitment and retention of underrepresented minorities (particularly women) in STEM fields through the Pacific Northwest Louis Stokes Alliance for Minority Participation.This project will lead to the design and development of a new, scalable, visual analytics platform suitable for hypothesis extraction and refinement from complex phenomics data sets. Focus on hypothesis extraction is critical in the context of phenomics data sets because much of the high-throughput sensing data being generated in crop fields are generated in the absence of specifically formulated hypotheses. Extracting plausible hypotheses from the data represents an important but tedious task. To this end, this project will apply and develop new capabilities using emerging advanced algorithmic principles, particularly from the branch of mathematics called algebraic topology that studies shapes and structure of complex data. The research objectives are three-fold. First, the project will employ and extend emerging algorithmic techniques from algebraic topology to decode the structure of large, complex phenomics data. Second, an interactive visual analytic platform will be developed to facilitate knowledge discovery using the extracted topological structures. Lastly, the quality and validity of a new visual analytic platform designed by this team will be tested using real-world maize data sets as well as simulated inputs as testbeds. The developed framework will encode functions for scientists to delineate hypotheses of three kinds: i) genetic characterization of single complex traits; ii) genetic characterization of multiple traits that share potentially pleiotropic effects; and iii) decoding and detailed characterization of genotype-by-environmental interactions, in particular, through a collaborative pilot study of maize flowering and growth traits. The expected significance of the proposed work is that biologists will be able to extract different types of testable hypotheses from plant phenomics data sets by employing a new class of visual analytic tools, and thus obtain a deeper understanding of the interactions among genotypes, environments and phenotypes. The project is potentially transformative in two ways: i) it will introduce advanced mathematical and computational principles into mainstream phenomic data analysis; and ii) it will usher in a new era where biologists spearhead data-driven hypothesis extraction and discovery with the aid of interactive, informative, and intuitive tools. The project will have a direct impact on the state of software in phenomics for fundamental data-driven discovery. To facilitate broader community adoption, the project will integrate the tools into the CyVerse Institute, and to a community phenomics software outlet. It will also lead to the development of automated scientific workflows. Project website: http://tdaphenomics.eecs.wsu.edu/

了解环境相互作用的基因如何导致特定的表型是现代生物学的核心目标，并对作物管理等事物产生了现实世界的影响。发展和管理成功的作物实践是一个与我们的国家粮食安全息息相关的目标。通过应用新颖的计算视觉分析方法，该项目旨在识别和揭示连接基因型，环境和表型相互作用的复杂网络。这些方法首先需要设计并开发到可以处理大量作物现象数据的可用软件应用程序中。高通量传感技术为许多植物特征（例如开花时间）收集了大量的现场数据，与作物的发育和生产有关。这里使用的玉米品种来自多种在各种环境条件下生长的基因型，以提供最广泛的条件以理解相互作用。所得数据集的规模和复杂性都在迅速增长，但是提取知识和催化科学发现所需的分析工具已显着落后。该项目中要开发的方法代表了弥合这种迅速扩大鸿沟的系统尝试。该项目本质上是跨学科的，涉及计算机科学家，植物科学家和数学家之间的密切研究伙伴关系。 The research outcomes will be tightly integrated with education using a multipronged approach that includes, among others, postdoctoral and student training (graduates and undergraduates), curriculum development for a new campus-wide interdisciplinary undergraduate degree in Data Analytics, conference tutorials for training phenomics data practitioners, and contribution to the recruitment and retention of underrepresented minorities (particularly women) in STEM fields through西北太平洋路易斯·斯托克斯（Louis Stokes）少数群体参与。该项目将导致设计和开发一个新的，可扩展的，视觉分析平台，适合于复杂的现象数据集中的假设提取和改进。在现象数据集的背景下，关注假设提取至关重要，因为在没有明确提出的假设的情况下，在作物场中生成的许多高通量感应数据都是生成的。从数据中提取合理的假设是一项重要但繁琐的任务。为此，该项目将使用新兴的高级算法原理应用和开发新功能，尤其是从称为代数拓扑的数学分支，研究复杂数据的塑造和结构。研究目标是三倍。首先，该项目将采用和扩展新兴算法技术从代数拓扑来解码大型，复杂的现象学数据的结构。其次，将开发一个交互式视觉分析平台，以促进使用提取的拓扑结构来促进知识发现。最后，该团队设计的新型视觉分析平台的质量和有效性将使用现实世界中的玉米数据集以及模拟输入作为测试台进行测试。开发的框架将编码供科学家描述三种假设的功能：i）单个复杂性状的遗传表征； ii）多种特征的遗传表征，这些特征具有潜在的多效作用； iii）通过对玉米开花和生长特征的合作试点研究，尤其是逐环境相互作用的解码和详细表征。拟议工作的预期意义在于，生物学家将能够通过采用新的视觉分析工具来从植物现象数据集中提取不同类型的可检验的假设，从而更深入地了解基因型，环境和表型之间的相互作用。该项目可能通过两种方式具有变革性：i）将高级数学和计算原理引入主流现象数据分析； ii）它将迎来一个新时代，生物学家将数据驱动的假设提取和借助交互式，信息性和直观的工具带动了。该项目将直接影响基本数据驱动的发现现象状态的软件状态。为了促进更广泛的社区采用，该项目将将工具集成到Cyverse Institute，并将工具集成到社区现象软件渠道中。这也将导致自动化科学工作流的发展。项目网站：http：//tdaphenomics.eecs.wsu.edu/

项目成果

UMAP: uniform manifold approximation and projection

• DOI: 10.21105/joss.00861
• 发表时间: 2018-01-01
• 期刊: Journal of Open Source Software
• 作者: McInnes, L.;Healy, J.;Groβberger, L.
• 通讯作者: Groβberger, L.

Continuous toolpath planning in a graphical framework for sparse infill additive manufacturing

• DOI: 10.1016/j.cad.2020.102880
• 发表时间: 2020-10-01
• 期刊: COMPUTER-AIDED DESIGN
• 影响因子: 4.3
• 作者: Gupta, Prashant;Krishnamoorthy, Bala;Dreifus, Gregory
• 通讯作者: Dreifus, Gregory

Stitch Fix for Mapper and Information Gains, Research in Computational Topology, arXiv:2105.01961

映射器和信息增益的缝合修复，计算拓扑研究，arXiv：2105.01961

• 发表时间: 2021
• 期刊: ArXivorg
• 作者: Youjia Zhou, Nathaniel Saul

Interactive Machine Learning Heuristics in Learning from Users

交互式机器学习启发式向用户学习

• 发表时间: 2018
• 期刊: IEEE Vis Workshop
• 作者: E. Corbett, N. Saul

Pheno-mapper: an interactive toolbox for the visual exploration of phenomics data

• DOI: 10.1145/3459930.3469511
• 发表时间: 2021-06
• 期刊: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
• 作者: Youjia Zhou;M. Kamruzzaman;P. Schnable;Bala Krishnamoorthy;A. Kalyanaraman;Bei Wang