通过通道学习空间转录组学中的表型相关特征（小方法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.202570035

Chen-Kai Guo, Chen-Rui Xia, Guangdun Peng, Zhi-Jie Cao, Ge Gao

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引用次数: 0

摘要

空间特征识别封面图片描绘了一个新的深度学习框架，通道，旨在自动识别大规模异质空间转录组组队列中与表型相关的空间特征，图式地表示为连接不同拼图块的交织通道（空间转录组学记录细胞的真实空间位置，这一过程类似于将分散的拼图重建为原始景观）。更多内容见彭光敦、曹志杰、高戈及其同事的2401451号文章。

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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Learning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE (Small Methods 5/2025)

Spatial Signature Identification

The cover image depicts a novel deep learning framework, PASSAGE, designed to automatically identify phenotype-associated spatial signatures across large-scale heterogeneous spatial transcriptomics cohorts, schematically represented by intertwining passages connecting different puzzle pieces (spatial transcriptomics record the ground-truth spatial location of cells, a process akin to reconstructing a scattered puzzle into its original landscape). More in article number 2401451 by Guangdun Peng, Zhi-Jie Cao, and Ge Gao and co-workers.

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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.