scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We...

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Autores principales:	Qianqian Song, Jing Su, Lance D. Miller, Wei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Single-cell RNA sequencing Consensus clustering Latent space Markov Chain Monte Carlo Maximum likelihood approach Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e9b4bee5e52b4358ac8765ba2fdd26c3
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id	oai:doaj.org-article:e9b4bee5e52b4358ac8765ba2fdd26c3
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spelling	oai:doaj.org-article:e9b4bee5e52b4358ac8765ba2fdd26c32021-11-16T04:09:19ZscLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets1672-022910.1016/j.gpb.2020.09.002https://doaj.org/article/e9b4bee5e52b4358ac8765ba2fdd26c32021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S167202292030142Xhttps://doaj.org/toc/1672-0229In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.Qianqian SongJing SuLance D. MillerWei ZhangElsevierarticleSingle-cell RNA sequencingConsensus clusteringLatent spaceMarkov Chain Monte CarloMaximum likelihood approachBiology (General)QH301-705.5ENGenomics, Proteomics & Bioinformatics, Vol 19, Iss 2, Pp 330-341 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Single-cell RNA sequencing Consensus clustering Latent space Markov Chain Monte Carlo Maximum likelihood approach Biology (General) QH301-705.5
spellingShingle	Single-cell RNA sequencing Consensus clustering Latent space Markov Chain Monte Carlo Maximum likelihood approach Biology (General) QH301-705.5 Qianqian Song Jing Su Lance D. Miller Wei Zhang scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
description	In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.
format	article
author	Qianqian Song Jing Su Lance D. Miller Wei Zhang
author_facet	Qianqian Song Jing Su Lance D. Miller Wei Zhang
author_sort	Qianqian Song
title	scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
title_short	scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
title_full	scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
title_fullStr	scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
title_full_unstemmed	scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets
title_sort	sclm: automatic detection of consensus gene clusters across multiple single-cell datasets
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e9b4bee5e52b4358ac8765ba2fdd26c3
work_keys_str_mv	AT qianqiansong sclmautomaticdetectionofconsensusgeneclustersacrossmultiplesinglecelldatasets AT jingsu sclmautomaticdetectionofconsensusgeneclustersacrossmultiplesinglecelldatasets AT lancedmiller sclmautomaticdetectionofconsensusgeneclustersacrossmultiplesinglecelldatasets AT weizhang sclmautomaticdetectionofconsensusgeneclustersacrossmultiplesinglecelldatasets
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scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

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