PDF(Pubmed)
Abstract：
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
摘要：
近年来,数据驱动的细胞-细胞通信推断有助于揭示跨细胞类型的协调生物过程。这里,我们集成了两个工具，利亚纳和张量细胞2细胞，which,当合并时，可以部署多种现有方法和资源，以实现跨多个样本的小区-小区通信程序的稳健和灵活的识别。在这项工作中,我们展示了我们的工具的集成如何促进推断细胞-细胞通信的方法的选择，并随后执行无监督的去卷积以获得和总结生物学见解。我们解释了如何在Python和R中一步一步地执行分析，并提供在线教程，详细说明可在https://ccc协议中获得。readthedocs.io/.这个工作流程通常需要1.5h从安装到在图形处理单元启用的计算机上的下游可视化完成~63,000个细胞的数据集，10种细胞类型，12个样本
