Department of Bioinformatics, Centre de Recherches en Cancérologie de Toulouse, INSERM, Occitanie 31100, France.
Bioinformatics. 2021 Nov 5;37(21):3989-3991. doi: 10.1093/bioinformatics/btab490.
Networks provide a powerful framework to analyze spatial omics experiments. However, we lack tools that integrate several methods to easily reconstruct networks for further analyses with dedicated libraries. In addition, choosing the appropriate method and parameters can be challenging. We propose tysserand, a Python library to reconstruct spatial networks from spatially resolved omics experiments. It is intended as a common tool to which the bioinformatics community can add new methods to reconstruct networks, choose appropriate parameters, clean resulting networks and pipe data to other libraries.
tysserand software and tutorials with a Jupyter notebook to reproduce the results are available at https://github.com/VeraPancaldiLab/tysserand.
Supplementary data are available at Bioinformatics online.
网络为分析空间组学实验提供了一个强大的框架。然而，我们缺乏能够集成多种方法的工具，以便使用专用库轻松地重建网络以进行进一步分析。此外，选择合适的方法和参数可能具有挑战性。我们提出了 tysserand，这是一个用于从空间分辨组学实验中重建空间网络的 Python 库。它旨在作为一个通用工具，供生物信息学社区添加新的方法来重建网络、选择适当的参数、清理生成的网络，并将数据传输到其他库。
tysserand 软件和带有 Jupyter 笔记本的教程可在 https://github.com/VeraPancaldiLab/tysserand 上获得，以重现结果。
补充数据可在《生物信息学》在线获得。
