最近的研究表明，细胞邻域在癌症和糖尿病等生物事件的演变中发挥着重要作用。因此，从空间分辨率的单细胞转录组数据或单细胞分辨率的组织成像数据中准确有效地识别细胞邻域至关重要。在这项工作中，我们开发了 CNTools，这是一个计算工具箱，用于对带注释的细胞图像进行端到端细胞邻域分析，包括识别和分析步骤。它包括最先进的细胞邻域识别方法和识别后平滑技术，以及我们新提出的细胞邻域嵌入（CNE）方法和朴素平滑技术，以及几种已建立的下游分析方法。我们在三个真实的 CODEX 数据集上应用 CNTools，并使用平滑技术定量和定性评估识别方法。它表明，采用 Naive Smoothing 的 CNE 总体优于其他方法，并揭示了更令人信服的生物学见解。我们还提供了如何根据输入数据选择适当的识别方法和平滑技术的建议。版权所有：© 2024 Tai et al.这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data.Copyright: © 2024 Tao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.