Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning
Abstract The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes t...
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The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference. |
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The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.</description><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbac586</identifier><identifier>PMID: 36585783</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Artificial neural networks ; Data sources ; Effectiveness ; Embedding ; Exome Sequencing ; Gene Expression ; Gene Regulatory Networks ; Gene sequencing ; Inference ; Learning ; Neural networks ; Neural Networks, Computer ; Regulatory mechanisms (biology)</subject><ispartof>Briefings in bioinformatics, 2023-01, Vol.24 (1)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c348t-36439a2cc0ed14d0f6ffd529225acd0e330efa1ba94c99ec31d3dab57e36293e3</citedby><cites>FETCH-LOGICAL-c348t-36439a2cc0ed14d0f6ffd529225acd0e330efa1ba94c99ec31d3dab57e36293e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,1605,27928,27929</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bib/bbac586$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36585783$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Zerun</creatorcontrib><creatorcontrib>Ou-Yang, Le</creatorcontrib><title>Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Data sources</subject><subject>Effectiveness</subject><subject>Embedding</subject><subject>Exome Sequencing</subject><subject>Gene Expression</subject><subject>Gene Regulatory Networks</subject><subject>Gene sequencing</subject><subject>Inference</subject><subject>Learning</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Regulatory mechanisms (biology)</subject><issn>1467-5463</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90c9LHDEUB_BQKtVue-pdAoVSkKn5nclRRFtB8GLPQyZ5s0RnkjGZWbv_vbPs1oMHT-8dPnx5vC9C3yj5RYnh521oz9vWOlmrD-iECq0rQaT4uNuVrqRQ_Bh9LuWBEEZ0TT-hY65kLXXNT9DTTewg5xDXeA0RcIb13Nsp5S2OMD2n_Fhwl9OAy0J6qBz0_V7CvzFDKSFF7O1k8SZY7AFGPMz9FKpNgGfsUpyyLVPYAO7B5riEfEFHne0LfD3MFfp7fXV_-ae6vft9c3lxWzku6qniSnBjmXMEPBWedKrrvGSGMWmdJ8A5gc7S1hrhjAHHqefetlIDV8xw4Cv0c5875vQ0Q5maIZTd-TZCmkvDtDRGaq3YQr-_oQ9pznG5ruGUcqEYqdWizvbK5VRKhq4Zcxhs3jaUNLsmmqWJ5tDEok8PmXM7gH-1_1-_gB97kObx3aQXkuqUOw</recordid><startdate>20230119</startdate><enddate>20230119</enddate><creator>Lin, Zerun</creator><creator>Ou-Yang, Le</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20230119</creationdate><title>Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning</title><author>Lin, Zerun ; Ou-Yang, Le</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-36439a2cc0ed14d0f6ffd529225acd0e330efa1ba94c99ec31d3dab57e36293e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Data sources</topic><topic>Effectiveness</topic><topic>Embedding</topic><topic>Exome Sequencing</topic><topic>Gene Expression</topic><topic>Gene Regulatory Networks</topic><topic>Gene sequencing</topic><topic>Inference</topic><topic>Learning</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Regulatory mechanisms (biology)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Zerun</creatorcontrib><creatorcontrib>Ou-Yang, Le</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lin, Zerun</au><au>Ou-Yang, Le</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2023-01-19</date><risdate>2023</risdate><volume>24</volume><issue>1</issue><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>Abstract
The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36585783</pmid><doi>10.1093/bib/bbac586</doi></addata></record> |
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subjects | Algorithms Artificial neural networks Data sources Effectiveness Embedding Exome Sequencing Gene Expression Gene Regulatory Networks Gene sequencing Inference Learning Neural networks Neural Networks, Computer Regulatory mechanisms (biology) |
title | Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning |
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