A Robust Kernel-Based Workflow for Niche Trajectory Analysis (Small Methods 5/2025)

IF 10.7 2区材料科学 Q1 CHEMISTRY, PHYSICAL

Small Methods Pub Date : 2025-05-24 DOI:10.1002/smtd.202570031

Wen Wang, 王文, Sujung Crystal Shin, Joselyn Cristina Chávez-Fuentes, Guo-Cheng Yuan

引用次数: 0

Abstract

Niche Trajectory Analyses

In article number 2401199, Guo-Cheng Yuan and co-workers develop a kernel-based machine learning method for niche trajectory analysis. This method enables biologists to dissect the spatially continuous variations of tissue microenvironment from spatial omics data. This method is technology agnostic and does not require cell type annotation information.

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基于鲁棒核的生态位轨迹分析工作流（Small Methods 5/2025）

生态位轨迹分析在文章编号2401199中，袁国成及其同事开发了一种基于核的机器学习方法用于生态位轨迹分析。这种方法使生物学家能够从空间组学数据中解剖组织微环境的空间连续变化。此方法与技术无关，不需要单元格类型注释信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Small Methods Materials Science-General Materials Science

CiteScore

17.40

自引率

1.60%

发文量

347

期刊介绍： Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.