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: 1661375
• 负责人: Bei Phillips
• 金额: $ 28.81万
• 依托单位: University of Utah
• 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=1661375&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/

了解基因与环境的相互作用如何导致特定的表型是现代生物学的核心目标，并对作物管理等事情产生现实影响。开发和管理成功的作物实践是一个与我们的国家粮食安全密切相关的目标。通过应用新的计算可视化分析方法，该项目旨在识别和解开连接基因型，环境和表型的复杂相互作用网络。这些方法首先需要设计和开发成可用的软件应用程序，可以处理大量的作物表型组学数据。高通量传感技术收集了大量与作物发育和生产相关的许多植物性状的田间数据，例如开花时间。这里使用的玉米品种来自多种基因型，这些基因型在各种环境条件下生长，以便为理解相互作用提供最广泛的条件。由此产生的数据集在规模和复杂性方面都在迅速增长，但提取知识和促进科学发现所需的分析工具却远远落后。本项目拟制定的方法是有系统地试图弥合这一迅速扩大的鸿沟。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。研究成果将与教育紧密结合，采用多管齐下的方法，其中包括博士后和学生培训（毕业生和本科生），数据分析新的校园跨学科本科学位的课程开发，培训表型组学数据从业者的会议教程，为征聘和留住任职人数不足的少数群体作出贡献通过太平洋西北路易斯斯托克斯少数民族参与联盟，在STEM领域（特别是妇女）。该项目将导致设计和开发一个新的，可扩展的，可视化分析平台，适用于从复杂的表型组学数据集中提取和细化假设。在表型组学数据集的背景下，对假设提取的关注是至关重要的，因为在作物田中生成的大部分高通量传感数据是在没有具体制定的假设的情况下生成的。从数据中提取合理的假设是一项重要但繁琐的任务。为此，该项目将应用和开发新的能力，使用新兴的先进算法原理，特别是来自数学的分支，称为代数拓扑，研究复杂数据的形状和结构。研究目标有三个方面。首先，该项目将采用和扩展代数拓扑学中的新兴算法技术，以解码大型复杂表型组学数据的结构。其次，将开发一个交互式可视化分析平台，以促进知识发现使用提取的拓扑结构。最后，该团队设计的新视觉分析平台的质量和有效性将使用真实世界的玉米数据集以及模拟输入作为测试平台进行测试。开发的框架将编码科学家描绘三类假设的功能：i）单一复杂性状的遗传特征; ii）共享潜在多效性效应的多个性状的遗传特征; iii）解码和详细表征基因型与环境的相互作用，特别是通过玉米开花和生长性状的合作试点研究。该工作的预期意义在于，生物学家将能够通过采用一类新型视觉分析工具从植物表型组学数据集中提取不同类型的可检验假设，从而更深入地了解基因型、环境和表型之间的相互作用。该项目在两个方面具有潜在的变革性：i）它将把先进的数学和计算原理引入主流的表型数据分析; ii）它将开创一个新时代，生物学家将在交互式，信息丰富和直观的工具的帮助下，率先进行数据驱动的假设提取和发现。该项目将直接影响表型组学软件的状态，以进行基础数据驱动的发现。为了促进更广泛的社区采用，该项目将把这些工具集成到CyVerse研究所和社区表型组学软件出口。它还将导致自动化科学工作流程的发展。项目网址：http://tdaphenomics.eecs.wsu.edu/

项目成果

Topological inference of manifolds with boundary

有边界流形的拓扑推理

• DOI: 10.1016/j.comgeo.2019.101606
• 发表时间: 2020
• 期刊: Computational Geometry
• 作者: Wang, Yuan;Wang, Bei
• 通讯作者: Wang, Bei

Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data

• DOI: 10.1109/pacificvis52677.2021.00021
• 发表时间: 2020-11
• 期刊: 2021 IEEE 14th Pacific Visualization Symposium (PacificVis)
• 作者: Youjia Zhou;N. Chalapathi;Archit Rathore;Yaodong Zhao;Bei Wang

VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations

• DOI: 10.1145/3604433
• 发表时间: 2023-06
• 期刊: ACM Transactions on Interactive Intelligent Systems
• 影响因子: 3.4
• 作者: Archit Rathore;Yan Zheng;Chin-Chia Michael Yeh

Discrete Stratified Morse Theory: A User's Guide

离散分层莫尔斯电码理论：用户指南

• DOI: 10.4230/lipics.socg.2018.54
• 发表时间: 2018
• 期刊: 34th International Symposium on Computational Geometry (SoCG 2018
• 作者: Knudson, Kevin;Wang, Bei
• 通讯作者: Wang, Bei

On homotopy types of Vietoris–Rips complexes of metric gluings

• DOI: 10.1007/s41468-020-00054-y
• 发表时间: 2017-12
• 期刊: Journal of Applied and Computational Topology
• 作者: Michal Adamaszek;Henry Adams;Ellen Gasparovic;Maria Gommel;Emilie Purvine;R. Sazdanovic;Bei Wang