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

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 gener...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Dongfang, Hou, Siyu, Zhang, Lei, Wang, Xiliang, Liu, Baolin, Zhang, Zemin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891139/
https://www.ncbi.nlm.nih.gov/pubmed/33602306
http://dx.doi.org/10.1186/s13059-021-02280-8
_version_ 1783652643275538432
author Wang, Dongfang
Hou, Siyu
Zhang, Lei
Wang, Xiliang
Liu, Baolin
Zhang, Zemin
author_facet Wang, Dongfang
Hou, Siyu
Zhang, Lei
Wang, Xiliang
Liu, Baolin
Zhang, Zemin
author_sort Wang, Dongfang
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02280-8.
format Online
Article
Text
id pubmed-7891139
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-78911392021-02-22 iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks Wang, Dongfang Hou, Siyu Zhang, Lei Wang, Xiliang Liu, Baolin Zhang, Zemin Genome Biol Method 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02280-8. BioMed Central 2021-02-18 /pmc/articles/PMC7891139/ /pubmed/33602306 http://dx.doi.org/10.1186/s13059-021-02280-8 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Wang, Dongfang
Hou, Siyu
Zhang, Lei
Wang, Xiliang
Liu, Baolin
Zhang, Zemin
iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks
title iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks
title_full iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks
title_fullStr iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks
title_full_unstemmed iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks
title_short iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks
title_sort imap: integration of multiple single-cell datasets by adversarial paired transfer networks
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891139/
https://www.ncbi.nlm.nih.gov/pubmed/33602306
http://dx.doi.org/10.1186/s13059-021-02280-8
work_keys_str_mv AT wangdongfang imapintegrationofmultiplesinglecelldatasetsbyadversarialpairedtransfernetworks
AT housiyu imapintegrationofmultiplesinglecelldatasetsbyadversarialpairedtransfernetworks
AT zhanglei imapintegrationofmultiplesinglecelldatasetsbyadversarialpairedtransfernetworks
AT wangxiliang imapintegrationofmultiplesinglecelldatasetsbyadversarialpairedtransfernetworks
AT liubaolin imapintegrationofmultiplesinglecelldatasetsbyadversarialpairedtransfernetworks
AT zhangzemin imapintegrationofmultiplesinglecelldatasetsbyadversarialpairedtransfernetworks

Ejemplares similares