{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:53:17Z","timestamp":1762253597128,"version":"3.37.3"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"S6","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T00:00:00Z","timestamp":1605657600000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. In this paper we propose a multi-view Hessian regularization based symmetric nonnegative matrix factorization algorithm (MHSNMF) for clustering heterogeneous microbiome data. Compared with many existing approaches, the advantages of MHSNMF lie in: (1) MHSNMF combines multiple Hessian regularization to leverage the high-order information from the same cohort of instances with multiple representations; (2) MHSNMF utilities the advantages of SNMF and naturally handles the complex relationship among microbiome samples; (3) uses the consensus matrix obtained by MHSNMF, we also design a novel approach to predict the classification of new microbiome samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We conduct extensive experiments on two real-word datasets (Three-source dataset and Human Microbiome Plan dataset), the experimental results show that the proposed MHSNMF algorithm outperforms other baseline and state-of-the-art methods. Compared with other methods, MHSNMF achieves the best performance (accuracy: 95.28%, normalized mutual information: 91.79%) on microbiome data. It suggests the potential application of MHSNMF in microbiome data analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Results show that the proposed MHSNMF algorithm can effectively combine the phylogenetic, transporter, and metabolic profiles into a unified paradigm to analyze the relationships among different microbiome samples. Furthermore, the proposed prediction method based on MHSNMF has been shown to be effective in judging the types of new microbiome samples.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-020-03555-w","type":"journal-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T04:14:07Z","timestamp":1605672847000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-3362","authenticated-orcid":false,"given":"Yuanyuan","family":"Ma","sequence":"first","affiliation":[]},{"given":"Junmin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yingjun","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,18]]},"reference":[{"issue":"7164","key":"3555_CR1","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1038\/nature06244","volume":"449","author":"PJ Turnbaugh","year":"2007","unstructured":"Turnbaugh PJ, Ley RE, Hamady M, Fraserliggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449(7164):804\u201310.","journal-title":"Nature"},{"issue":"3","key":"3555_CR2","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.chom.2014.08.014","volume":"16","author":"IHN Consortium","year":"2014","unstructured":"Consortium IHN. The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe. 2014;16(3):276.","journal-title":"Cell Host Microbe"},{"issue":"7285","key":"3555_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/nature08821","volume":"464","author":"J Qin","year":"2010","unstructured":"Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59\u201365.","journal-title":"Nature"},{"issue":"2","key":"3555_CR4","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1109\/TCBB.2015.2440261","volume":"14","author":"X Jiang","year":"2017","unstructured":"Jiang X, Hu X, Xu W. Microbiome data representation by joint nonnegative matrix factorization with Laplacian regularization. IEEE\/ACM Trans Comput Biol Bioinformatics. 2017;14(2):353\u20139.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinformatics"},{"key":"3555_CR5","first-page":"423","volume-title":"European conference on machine learning","author":"D Greene","year":"2009","unstructured":"Greene D, Cunningham P. A matrix factorization approach for integrating multiple data views. In: European conference on machine learning; 2009. p. 423\u201338."},{"key":"3555_CR6","first-page":"252","volume-title":"Proceedings of the 2013 SIAM International Conference on Data Mining","author":"J Liu","year":"2013","unstructured":"Liu J, Wang C, Gao J, Han J. Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 2013 SIAM International Conference on Data Mining; 2013. p. 252\u201360."},{"issue":"13","key":"3555_CR7","doi-asserted-by":"publisher","first-page":"6606","DOI":"10.1093\/nar\/gkz488","volume":"47","author":"L Zhang","year":"2019","unstructured":"Zhang L, Zhang S. Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorization. Nucleic Acids Res. 2019;47(13):6606\u201317.","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"3555_CR8","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2010.231","volume":"33","author":"D Cai","year":"2011","unstructured":"Cai D, He X, Han J, Huang TS. Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell. 2011;33(8):1548\u201360.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"3555_CR9","doi-asserted-by":"publisher","first-page":"5967","DOI":"10.1093\/nar\/gky440","volume":"46","author":"J Chen","year":"2018","unstructured":"Chen J, Zhang S. Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization. Nucleic Acids Res. 2018;46(12):5967\u201376.","journal-title":"Nucleic Acids Res"},{"key":"3555_CR10","first-page":"979","volume-title":"Neural information processing systems","author":"KI Kim","year":"2009","unstructured":"Kim KI, Steinke F, Hein M. Semi-supervised regression using hessian energy with an application to semi-supervised dimensionality reduction. In: Neural information processing systems; 2009. p. 979\u201387."},{"key":"3555_CR11","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ymeth.2016.06.017","volume":"111","author":"Y Ma","year":"2016","unstructured":"Ma Y, Hu X, He T, Jiang X. Hessian regularization based symmetric nonnegative matrix factorization for clustering gene expression and microbiome data. Methods. 2016;111:80\u20134.","journal-title":"Methods"},{"key":"3555_CR12","first-page":"106","volume-title":"Siam international conference on data mining","author":"D Kuang","year":"2012","unstructured":"Kuang D, Ding CHQ, Park H. Symmetric nonnegative matrix factorization for graph clustering. In: Siam international conference on data mining; 2012. p. 106\u201317."},{"key":"3555_CR13","doi-asserted-by":"publisher","unstructured":"Long B, Zhang Z, Yu PS. Co-clustering by block value decomposition. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. New York: ACM; 2005. p. 635\u201340. https:\/\/doi.org\/10.1145\/1081870.1081949.","DOI":"10.1145\/1081870.1081949"},{"key":"3555_CR14","first-page":"1601","volume-title":"Advances in neural information processing systems","author":"L Zelnikmanor","year":"2005","unstructured":"Zelnikmanor L, Perona P. Self-tuning spectral clustering. In: Advances in neural information processing systems; 2005. p. 1601\u20138."},{"issue":"10","key":"3555_CR15","doi-asserted-by":"publisher","first-page":"5591","DOI":"10.1073\/pnas.1031596100","volume":"100","author":"D Donoho","year":"2003","unstructured":"Donoho D, Grimes C. Hessian eigenmaps: locally linear embedding techniques for high dimensional data. Proc Natl Acad Sci. 2003;100(10):5591\u20136.","journal-title":"Proc Natl Acad Sci"},{"issue":"7","key":"3555_CR16","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TIP.2013.2255302","volume":"22","author":"W Liu","year":"2013","unstructured":"Liu W, Tao D. Multiview hessian regularization for image annotation. IEEE Trans Image Process. 2013;22(7):2676\u201387.","journal-title":"IEEE Trans Image Process"},{"issue":"99","key":"3555_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCBB.2017.2756628","volume":"PP","author":"Y Ma","year":"2017","unstructured":"Ma Y, Hu X, He T, Jiang X. Clustering and integrating of heterogeneous microbiome data by joint symmetric nonnegative matrix factorization with laplacian regularization. IEEE\/ACM Trans Comput Biol Bioinformatics. 2017;PP(99):1\u20131. https:\/\/doi.org\/10.1109\/TCBB.2017.2756628.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinformatics"},{"key":"3555_CR18","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1145\/1291233.1291431","volume-title":"ACM International Conference on Multimedia","author":"M Wang","year":"2007","unstructured":"Wang M, Hua XS, Yuan X, Song Y, Dai LR. Optimizing multi-graph learning: towards a unified video annotation scheme. In: ACM International Conference on Multimedia; 2007. p. 862\u201371."},{"issue":"6","key":"3555_CR19","doi-asserted-by":"publisher","first-page":"1438","DOI":"10.1109\/TSMCB.2009.2039566","volume":"40","author":"T Xia","year":"2010","unstructured":"Xia T, Tao D, Mei T, Zhang Y. Multiview spectral embedding. IEEE Trans Syst Man Cybernetics Part B. 2010;40(6):1438\u201346.","journal-title":"IEEE Trans Syst Man Cybernetics Part B"},{"key":"3555_CR20","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/978-3-642-04180-8_45","volume-title":"European conference on machine learning and knowledge discovery in databases","author":"D Greene","year":"2009","unstructured":"Greene D. A matrix factorization approach for integrating multiple data views. In: European conference on machine learning and knowledge discovery in databases; 2009. p. 423\u201338."},{"issue":"7402","key":"3555_CR21","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/nature11234","volume":"486","author":"C Huttenhower","year":"2012","unstructured":"Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, Creasy HH, Earl AM, Fitzgerald MG, Fulton RS. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207\u201314.","journal-title":"Nature"},{"key":"3555_CR22","first-page":"267","volume-title":"International ACM sigir conference on research and development in information retrieval","author":"W Xu","year":"2003","unstructured":"Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization. In: International ACM sigir conference on research and development in information retrieval; 2003. p. 267\u201373."},{"key":"3555_CR23","first-page":"92","volume-title":"Conference on learning theory","author":"A Blum","year":"1998","unstructured":"Blum A, Mitchell TM. Combining labeled and unlabeled data with co-training. In: Conference on learning theory; 1998. p. 92\u2013100."},{"issue":"3","key":"3555_CR24","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nmeth.2810","volume":"11","author":"B Wang","year":"2014","unstructured":"Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibekains B, Goldenberg A. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333\u20137.","journal-title":"Nat Methods"},{"issue":"4","key":"3555_CR25","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1016\/j.patcog.2007.09.010","volume":"41","author":"C Boutsidis","year":"2008","unstructured":"Boutsidis C, Gallopoulos E. SVD based initialization: a head start for nonnegative matrix factorization. Pattern Recogn. 2008;41(4):1350\u201362.","journal-title":"Pattern Recogn"},{"issue":"9","key":"3555_CR26","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1038\/nrmicro2859","volume":"10","author":"IB Jeffery","year":"2012","unstructured":"Jeffery IB, Claesson MJ, O'toole PW, Shanahan F. Categorization of the gut microbiota: enterotypes or gradients? Nat Rev Microbiol. 2012;10(9):591.","journal-title":"Nat Rev Microbiol"},{"key":"3555_CR27","doi-asserted-by":"publisher","unstructured":"Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet GR, Levy R, Vasconcelos N. A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM international conference on multimedia. New York: ACM; 2010. p. 251\u201360. https:\/\/doi.org\/10.1145\/1873951.1873987.","DOI":"10.1145\/1873951.1873987"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03555-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-03555-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03555-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T17:04:50Z","timestamp":1618419890000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03555-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":27,"journal-issue":{"issue":"S6","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["3555"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03555-w","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2020,11]]},"assertion":[{"value":"19 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"234"}}