MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis
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RESEARCH
Open Access
MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis Yuanyuan Ma1* , Junmin Zhao2 and Yingjun Ma3 From 15th International Symposium on Bioinformatics Research and Applications (ISBRA'19) Barcelona, Spain. 3-6 June 2019
* Correspondence: [email protected] 1 School of Computer & Information Engineering, Anyang Normal University, Anyang, China Full list of author information is available at the end of the article
Abstract Background: 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. Results: 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. Conclusions: 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. Keywords: Symmetric nonnegative matrix factorization, Hessian regularization, Multiview clustering, Human microbiome
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