iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks.

IF 12.3 1区 生物学 Q1 Agricultural and Biological Sciences Genome Biology Pub Date : 2021-02-18 DOI:10.1186/s13059-021-02280-8
Dongfang Wang, Siyu Hou, Lei Zhang, Xiliang Wang, Baolin Liu, Zemin Zhang
{"title":"iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks.","authors":"Dongfang Wang, Siyu Hou, Lei Zhang, Xiliang Wang, Baolin Liu, Zemin Zhang","doi":"10.1186/s13059-021-02280-8","DOIUrl":null,"url":null,"abstract":"<p><p>The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.</p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3000,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891139/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13059-021-02280-8","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.

Abstract Image

Abstract Image

Abstract Image

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
iMAP:通过对抗性配对转移网络整合多个单细胞数据集。
整合来自多个来源的单细胞 RNA 序列数据集对于破译复杂生物系统中细胞间的异质性和相互作用至关重要。我们基于深度自动编码器和生成式对抗网络,提出了一种新颖的无监督批量效应去除框架,称为 iMAP。与目前的方法相比,iMAP 在可靠地检测批特异性细胞和有效地混合批共享细胞类型的分布方面都表现出卓越、稳健和可扩展的性能。将 iMAP 应用于来自 Smart-seq2 和 10x Genomics 这两个平台的肿瘤微环境数据集,我们发现 iMAP 可以利用这两个平台的优势发现新的细胞-细胞相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Genome Biology
Genome Biology BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
25.50
自引率
3.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Genome Biology is a leading research journal that focuses on the study of biology and biomedicine from a genomic and post-genomic standpoint. The journal consistently publishes outstanding research across various areas within these fields. With an impressive impact factor of 12.3 (2022), Genome Biology has earned its place as the 3rd highest-ranked research journal in the Genetics and Heredity category, according to Thomson Reuters. Additionally, it is ranked 2nd among research journals in the Biotechnology and Applied Microbiology category. It is important to note that Genome Biology is the top-ranking open access journal in this category. In summary, Genome Biology sets a high standard for scientific publications in the field, showcasing cutting-edge research and earning recognition among its peers.
期刊最新文献
DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates. Seqrutinator: scrutiny of large protein superfamily sequence datasets for the identification and elimination of non-functional homologues. Systemic interindividual DNA methylation variants in cattle share major hallmarks with those in humans. TaqTth-hpRNA: a novel compact RNA-targeting tool for specific silencing of pathogenic mRNA. Benchmarking computational variant effect predictors by their ability to infer human traits.
×
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