title: Microbiome Data Representation by Joint Nonnegative Matrix Factorization with Laplacian Regularization.
abstract: Microbiome datasets are often comprised of different representations or views which provide complementary information to understand microbial communities, such as metabolic pathways, taxonomic assignments, and gene families. Data integration methods including approaches based on nonnegative matrix factorization (NMF) combine multi-view data to create a comprehensive view of a given microbiome study by integrating multi-view information. In this paper, we proposed a novel variant of NMF which called Laplacian regularized joint non-negative matrix factorization (LJ-NMF) for integrating functional and phylogenetic profiles from HMP. We compare the performance of this method to other variants of NMF. The experimental results indicate that the proposed method offers an efficient framework for microbiome data analysis.
authors.forename: Xingpeng
authors.initials: X
authors.lastname: Jiang
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Hu
authors.forename: Weiwei
authors.initials: W
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling; D006801:Humans; D064307:Microbiota / Q000235:genetics / Q000502:physiology; D008962:Models, Theoretical; D010802:Phylogeny