Université Paris Cité, INSERM U976 HIPI, Paris, F-75010, France.
Université Paris-Saclay, Saint Aubin, F-91190, France.
Bioinformatics. 2024 Mar 4;40(3). doi: 10.1093/bioinformatics/btae089.
Several methods have been developed in the past years to infer cell-cell communication networks from transcriptomic data based on ligand and receptor expression. Among them, ICELLNET is one of the few approaches to consider the multiple subunits of ligands and receptors complexes to infer and quantify cell communication. In here, we present a major update of ICELLNET. As compared to its original implementation, we (i) drastically expanded the ICELLNET ligand-receptor database from 380 to 1669 biologically curated interactions, (ii) integrated important families of communication molecules involved in immune crosstalk, cell adhesion, and Wnt pathway, (iii) optimized ICELLNET framework for single-cell RNA sequencing data analyses, (iv) provided new visualizations of cell-cell communication results to facilitate prioritization and biological interpretation. This update will broaden the use of ICELLNET by the scientific community in different biological fields.
ICELLNET package is implemented in R. Source code, documentation and tutorials are available on GitHub (https://github.com/soumelis-lab/ICELLNET).
过去几年中，已经开发了多种方法来基于配体和受体表达从转录组数据推断细胞间通讯网络。其中，ICELINET 是为数不多的考虑配体和受体复合物的多个亚基来推断和量化细胞通讯的方法之一。在这里，我们展示了 ICELLNET 的重大更新。与原始实现相比，我们（i）将 ICELLNET 配体-受体数据库从 380 个扩展到 1669 个经过生物验证的相互作用，（ii）整合了涉及免疫串扰、细胞黏附和 Wnt 途径的重要通讯分子家族，（iii）优化了 ICELLNET 框架以用于单细胞 RNA 测序数据分析，（iv）提供了细胞间通讯结果的新可视化，以促进优先级排序和生物学解释。此更新将扩大 ICELLNET 在不同生物领域的科学界的使用。
ICELLNET 包是用 R 实现的。源代码、文档和教程都可在 GitHub（https://github.com/soumelis-lab/ICELLNET）上获得。
