Tianyi Chen, Xindian Wei, Lianxin Xie, Yunfei Zhang, Cheng Liu, Wenjun Shen, Si Wu, Hau-San Wong
{"title":"SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction.","authors":"Tianyi Chen, Xindian Wei, Lianxin Xie, Yunfei Zhang, Cheng Liu, Wenjun Shen, Si Wu, Hau-San Wong","doi":"10.1093/bib/bbae523","DOIUrl":null,"url":null,"abstract":"<p><p>The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483138/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Briefings in bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bib/bbae523","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.
将单细胞 RNA 测序(scRNA-seq)数据空间重构为空间转录组学(ST)是一个快速发展的领域,它解决了将基因表达谱与其在组织内的空间起源对齐的重大挑战。由于固有的批次效应以及需要精确的基因表达表征以准确反映空间信息,这项任务变得非常复杂。为了应对这些挑战,我们开发了 SELF-Former,这是一种基于变换器的框架,它利用多尺度结构来学习基因表征,同时为重建相应的 ST 数据设计空间相关性约束。SELF-Former 擅长恢复 ST 数据的空间信息,并能有效缓解 scRNA-seq 和 ST 数据之间的批次效应。SELF-Former 的一个新颖之处是引入了基因过滤模块,通过选择对准确空间定位和重建至关重要的基因,大大增强了空间重建任务。SELF-Former 模块的卓越性能和有效性已在四个基准数据集上得到验证,使其成为空间重建任务中一种稳健有效的方法。SELF-Former 证明了自己有能力从 scRNA-seq 数据中提取有意义的基因表达信息,并将其准确映射到真实 ST 数据的空间环境中。我们的方法代表了该领域的重大进步,为空间重建提供了一种可靠的方法。
期刊介绍:
Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data.
The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.