SC2Spa：一种基于深度学习的方法，以细胞分辨率将转录组映射到空间起源。

IF 3.3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2025-06-02 DOI:10.1186/s12859-025-06173-6

Linbu Liao, Esha Madan, António M Palma, Hyobin Kim, Amit Kumar, Praveen Bhoopathi, Robert Winn, Jose Trevino, Paul Fisher, Cord Herbert Brakebusch, Gahyun Kim, Junil Kim, Rajan Gogna, Kyoung Jae Won

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引用次数: 0

摘要

背景：了解组织内的细胞异质性取决于对其空间背景的了解。然而，准确地将细胞映射到它们的空间坐标仍然是一个挑战。结果：我们提出了SC2Spa，这是一种基于深度学习的方法，可以从空间转录组学（ST）数据中学习复杂的空间关系。基准测试表明，SC2Spa优于其他预测因子，并能准确地从转录组中检测组织结构。SC2Spa成功地将单细胞RNA测序（scRNA-seq）映射到Visium分析中，为提高低分辨率ST数据的分辨率提供了一种方法。我们的测试表明，SC2Spa在各种ST技术中表现良好，并且对空间分辨率具有鲁棒性。此外，SC2Spa可以提示从以前的方法中无法识别的空间可变基因。结论：SC2Spa是一种可靠且准确的方法，可提供单细胞的空间位置并鉴定具有空间意义的基因。

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SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution.

Background: Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates.

Results: We present SC2Spa, a deep learning-based approach that learns intricate spatial relationships from spatial transcriptomics (ST) data. Benchmarking tests show that SC2Spa outperformed other predictors and accurately detected tissue architecture from transcriptome. SC2Spa successfully mapped single cell RNA sequencing (scRNA-seq) to Visium assay, providing an approach to enhance the resolution for low resolution ST data. Our test showed that SC2Spa performs well for various ST technologies and robust to spatial resolution. In addition, SC2Spa can suggest spatially variable genes that cannot be identified from previous approaches.

Conclusions: SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.