Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry.

中文翻译：

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PySpacell：用于细胞图像空间分析的 Python 包。

显微技术、序列杂交和质谱等技术可实现原位定量单细胞表型和分子测量。在单细胞水平上破译空间表型和分子效应是一项重大挑战，也是理解细胞间相互作用和微环境影响的关键。然而，空间信息通常被下游数据分析所忽视，这些分析通常将单细胞读出值视为进一步平均或聚类的独立测量值，从而忽略空间位置。通过这项工作，我们试图填补这一空白。我们开发了一个工具箱，允许人们测试贴壁细胞显微镜图像中空间效应的存在，并估计这种效应的空间尺度。提议的 Python 模块可用于细胞的任何光学显微镜图像以及其他类型的单细胞数据，例如原位转录组学或代谢组学。我们软件包的输入格式与 CellProfiler、Fiji 或 Icy 等图像分析工具的标准输出格式相匹配，因此使我们的工具箱易于使用，同时为各种应用提供了强大的统计方法。© 2019 国际细胞计量学促进会。还为广泛的应用提供了强大的统计方法。© 2019 国际细胞计量学促进会。还为广泛的应用程序提供了强大的统计方法。© 2019 国际细胞计量学促进会。