Graspy: A python package for rigorous statistical analysis of populations of attributed connectomes

Graspy：一个 python 包，用于对归因连接体群体进行严格的统计分析

• 批准号: 10012519
• 负责人: Carey Priebe
• 金额: $ 124.6万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10012519
• 关键词: Adoption; Algorithms; Behavioral Genetics; Brain; Code; Coin; Complement; Complex; Computer software; Data; Data Science; Data Set; Development; Discipline; Documentation; Educational workshop; Ensure; Foundations; Funding; Genes; Human; Image; Individual; Journals; Laboratories; Learning; Link; Machine Learning; Methodology; Methods; Modality; Modernization; Motivation; Neurosciences; Paper; Pathway Analysis; Phenotype; Physiology; Population; Population Analysis; Population Study; Property; PubMed; Publishing; Pythons; Research Personnel; Scientist; Statistical Data Interpretation; Statistical Methods; Statistical Study; Structure; Telecommunications; Testing; Visualization; Work; brain research; connectome; data pipeline; design; high dimensionality; high standard; open source; public repository; software development; theories; tool; user-friendly

PROJECT SUMMARY Overview: We will extend and develop implementations of foundational methods for analyzing populations of attributed connectomes. Our toolbox will enable brain scientists to (1) infer latent structure from individual connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships between connectomes and multivariate phenotypes. The methods we develop and extend will naturally overcome the challenges inherent in connectomics: high-dimensional non-Euclidean data with multi-level nonlinear interactions. Our implementations will comply with the highest open-source standards by: providing extensive online documentation and extended tutorials, hosting workshops to demonstrate our tools on an annual basis, and merging our implementations into commonly used packages such as scikit-learn [1], scipy [2], and networkx [3]. All of the code we develop is open source. We strive to ensure that our code is shared in accordance with the strictest guiding principles. We chose to implement these algorithms in Python due to its wide adoption in the neuroscience and data science fields. In particular, many other neuroscience tools applicable to connectomics, including NetworkX DiPy, mindboggle, nilearn, and nipy, are also implemented in Python. This will enable researchers to chain our analysis tools onto pre-existing pipelines for data preprocessing and visualization. Nonetheless, we feel that sharing our code in our own public repositories is insufficient for global reach. We have also begun reaching out to developers of the leading data science packages in python, including scipy, sklearn, networkx, scikit-image, and DiPy. For each of those packages, we have informal approval to begin integrating algorithms that we have developed. Those packages are collectively used by >220,000 other packages, so merging our algorithms into those packages will significantly extend our global reach. All researchers investigating connectomics, including all the authors of the 24,000 papers that mention the word “connectome”, will be able to apply state-of-the-art statistical theory and methods to their data. Currently, we have about 150 open source software projects on our NeuroData GitHub organization. Collectively, these projects get about 2,000 downloads and >11,000 views per month. As we incorporate additional functionality as described in this proposal, we expect far more researchers across disciplines and sectors will utilize our software. 20 ​ ​​ ​ ​​

项目概要 概述：我们将扩展和开发用于分析人口的基本方法的实施 归因连接体。我们的工具箱将使大脑科学家能够（1）从个体推断潜在结构 连接体，(2) 识别连接体群体中有意义的簇，以及 (3) 检测关系 连接体和多元表型之间。我们开发和扩展的方法自然会 克服连接组学固有的挑战：具有多层次的高维非欧几里得数据 非线性相互作用。我们的实施将通过以下方式遵守最高的开源标准： 广泛的在线文档和扩展教程，举办研讨会来展示我们的工具 每年一次，并将我们的实现合并到常用的包中，例如 scikit-learn [1]、scipy [2] 和网络 [3]。 我们开发的所有代码都是开源的。我们努力确保我们的代码按照以下标准共享 最严格的指导原则。我们选择用 Python 来实现这些算法，因为它在 神经科学和数据科学领域。特别是，许多其他适用于连接组学的神经科学工具， 包括 NetworkX DiPy、mindboggle、nilearn 和 nipy，也是用 Python 实现的。这将使 研究人员将我们的分析工具链接到预先存在的管道上，以进行数据预处理和可视化。 尽管如此，我们认为在我们自己的公共存储库中共享代码不足以实现全球影响力。我们 还开始接触 Python 中领先的数据科学包的开发人员，包括 scipy、 sklearn、networkx、scikit-image 和 DiPy。对于每个包，我们都已获得非正式批准才能开始 集成我们开发的算法。这些软件包被超过 220,000 个其他人共同使用 包，因此将我们的算法合并到这些包中将显着扩展我们的全球影响力。 所有研究连接组学的研究人员，包括提及该连接组学的 24,000 篇论文的所有作者 “连接组”一词将能够将最先进的统计理论和方法应用于他们的数据。现在， 我们的 NeuroData GitHub 组织上有大约 150 个开源软件项目。总的来说，这些 项目每月获得约 2,000 次下载和超过 11,000 次浏览。当我们整合附加功能时 正如本提案中所述，我们预计更多跨学科和跨领域的研究人员将利用我们的 软件。 20 ​ ​​ ​​​