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

项目总结 概述：我们将扩展和开发用于分析人口的基本方法的实现 属性连接符。我们的工具箱将使大脑科学家能够(1)从个体中推断潜在的结构 连接体，(2)在连接体群体中识别有意义的簇，以及(3)检测关系 连接体和多变量表型之间的关系。我们开发和扩展的方法将很自然地 克服连接学固有的挑战：具有多层次的高维非欧几里得数据 非线性相互作用。我们的实现将遵循最高的开源标准：提供 丰富的在线文档和扩展的教程，举办研讨会以演示我们在 并将我们的实现合并到常用的包中，如SCRICKIT-LEARN[1]、Scipy [2]和networkx[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 最严格的指导原则。我们选择用Python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学的神经科学工具， 包括NetworkX DiPy、Mindbogle、Nilearn和nipy，也是用Python语言实现的。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，我们觉得在我们自己的公共存储库中共享我们的代码不足以覆盖全球。我们 也开始接触领先的Python数据科学包的开发者，包括Scipy， Sknowlear、networkx、cikkit-Image和diPy。对于每一个套餐，我们都有非正式的批准开始 整合我们开发的算法。这些程序包由&>220,000其他人共同使用 因此，将我们的算法合并到这些包中将显著扩展我们的全球覆盖范围。 研究连接学的所有研究人员，包括提到 “Connectome”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。目前， 我们的NeuroData GitHub组织上有大约150个开源软件项目。总而言之，这些 该项目每月的下载量约为2000次，浏览量为1.1万次。当我们整合其他功能时 正如本提案中所描述的，我们预计将有更多跨学科和部门的研究人员利用我们的 软件。 20个 ​ ​​ ​​​

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 ​ ​​ ​ ​​