Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS Comput Biol. 2024 Aug 28;20(8):e1012344. doi: 10.1371/journal.pcbi.1012344. eCollection 2024 Aug.
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.
最近的研究表明，细胞群落在癌症和糖尿病等生物事件的进化中起着重要作用。因此，从空间分辨的单细胞转录组数据或单细胞分辨率组织成像数据中准确有效地识别细胞群落是至关重要的。在这项工作中，我们开发了 CNTools，这是一个用于注释细胞图像的端到端细胞群落分析的计算工具包，包括识别和分析步骤。它包含最先进的细胞群落识别方法和识别后的平滑技术，包括我们新提出的细胞邻居嵌入（CNE）方法和朴素平滑技术，以及几种已建立的下游分析方法。我们将 CNTools 应用于三个真实的 CODEX 数据集，并使用平滑技术对识别方法进行了定量和定性评估。结果表明，CNE 与朴素平滑技术总体上优于其他方法，并揭示了更有说服力的生物学见解。我们还根据输入数据提供了如何选择合适的识别方法和平滑技术的建议。
