spatiAlign:用于空间解析转录组学数据整合的无监督对比学习模型。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Chao Zhang, Lin Liu, Ying Zhang, Mei Li, Shuangsang Fang, Qiang Kang, Ao Chen, Xun Xu, Yong Zhang, Yuxiang Li
{"title":"spatiAlign:用于空间解析转录组学数据整合的无监督对比学习模型。","authors":"Chao Zhang, Lin Liu, Ying Zhang, Mei Li, Shuangsang Fang, Qiang Kang, Ao Chen, Xun Xu, Yong Zhang, Yuxiang Li","doi":"10.1093/gigascience/giae042","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.</p><p><strong>Findings: </strong>We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.</p><p><strong>Conclusions: </strong>In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258913/pdf/","citationCount":"0","resultStr":"{\"title\":\"spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.\",\"authors\":\"Chao Zhang, Lin Liu, Ying Zhang, Mei Li, Shuangsang Fang, Qiang Kang, Ao Chen, Xun Xu, Yong Zhang, Yuxiang Li\",\"doi\":\"10.1093/gigascience/giae042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.</p><p><strong>Findings: </strong>We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.</p><p><strong>Conclusions: </strong>In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.</p>\",\"PeriodicalId\":12581,\"journal\":{\"name\":\"GigaScience\",\"volume\":\"13 \",\"pages\":\"\"},\"PeriodicalIF\":11.8000,\"publicationDate\":\"2024-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258913/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GigaScience\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/gigascience/giae042\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giae042","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

背景对空间分辨率转录组学数据集进行整合分析有助于加深对复杂生物系统的理解。然而,整合多个组织切片给批次效应的去除带来了挑战,尤其是当切片是由不同技术测量或在不同时间采集时:我们提出的 spatiAlign 是一种无监督对比学习模型,它利用所有测量基因的表达和细胞的空间位置来整合多个组织切片。它不仅能在低维嵌入中,还能在重建的全表达空间中对多个数据集进行联合下游分析:在基准分析中,spatiAlign 在学习组织切片的联合和鉴别表征方面优于最先进的方法,而每个组织切片都可能具有复杂的批次效应或独特的生物特征。此外,我们还展示了 spatiAlign 在时间序列脑切片综合分析方面的优势,包括空间聚类、差异表达分析,特别是需要校正基因表达矩阵的轨迹推断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.

Background: Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.

Findings: We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.

Conclusions: In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信