空间转录组学的计算方法和挑战

IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Genomics, Proteomics & Bioinformatics Pub Date : 2023-02-01 DOI:10.1016/j.gpb.2022.10.001
Shuangsang Fang , Bichao Chen , Yong Zhang , Haixi Sun , Longqi Liu , Shiping Liu , Yuxiang Li , Xun Xu
{"title":"空间转录组学的计算方法和挑战","authors":"Shuangsang Fang ,&nbsp;Bichao Chen ,&nbsp;Yong Zhang ,&nbsp;Haixi Sun ,&nbsp;Longqi Liu ,&nbsp;Shiping Liu ,&nbsp;Yuxiang Li ,&nbsp;Xun Xu","doi":"10.1016/j.gpb.2022.10.001","DOIUrl":null,"url":null,"abstract":"<div><p>The development of <strong>spatial transcriptomics</strong> (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological <strong>data interpretation</strong>. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the <strong>data quality</strong>, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting <strong>multi-omics integration</strong> analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review <strong>computational approaches</strong> to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.</p></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 1","pages":"Pages 24-47"},"PeriodicalIF":11.5000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372921/pdf/","citationCount":"13","resultStr":"{\"title\":\"Computational Approaches and Challenges in Spatial Transcriptomics\",\"authors\":\"Shuangsang Fang ,&nbsp;Bichao Chen ,&nbsp;Yong Zhang ,&nbsp;Haixi Sun ,&nbsp;Longqi Liu ,&nbsp;Shiping Liu ,&nbsp;Yuxiang Li ,&nbsp;Xun Xu\",\"doi\":\"10.1016/j.gpb.2022.10.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The development of <strong>spatial transcriptomics</strong> (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological <strong>data interpretation</strong>. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the <strong>data quality</strong>, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting <strong>multi-omics integration</strong> analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review <strong>computational approaches</strong> to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.</p></div>\",\"PeriodicalId\":12528,\"journal\":{\"name\":\"Genomics, Proteomics & Bioinformatics\",\"volume\":\"21 1\",\"pages\":\"Pages 24-47\"},\"PeriodicalIF\":11.5000,\"publicationDate\":\"2023-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372921/pdf/\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics, Proteomics & Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1672022922001292\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics, Proteomics & Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1672022922001292","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 13

摘要

空间转录组学(ST)技术的发展将遗传学研究从单细胞数据水平转变为二维空间坐标系,并促进了对不同环境和器官中各种细胞亚群的组成和功能的研究。这些ST技术生成的包含空间基因表达信息的大规模数据,引发了对空间分辨方法的需求,以满足计算和生物数据解释的要求。这些要求包括处理数据的爆炸性增长以确定细胞水平和基因水平的表达,纠正内部批量效应和表达损失以提高数据质量,在单细胞和组织范围内进行有效的解释和深入的知识挖掘,以及进行多组学整合分析,为深入理解生物过程提供可扩展的框架。然而,专门为ST技术设计的满足这些要求的算法仍处于初级阶段。在这里,我们根据相应的问题和挑战回顾了解决这些问题的计算方法,并对算法开发提出了前瞻性的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Computational Approaches and Challenges in Spatial Transcriptomics

The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.

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
Genomics, Proteomics & Bioinformatics
Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
14.30
自引率
4.20%
发文量
844
审稿时长
61 days
期刊介绍: Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.
期刊最新文献
Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data. Identification of highly repetitive barley enhancers with long-range regulation potential via STARR-seq CpG island definition and methylation mapping of the T2T-YAO genome Pindel-TD: a tandem duplication detector based on a pattern growth approach SMARTdb: An Integrated Database for Exploring Single-cell Multi-omics Data of Reproductive Medicine
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1