Ko YoungJoon Kim, Sangsoo Pan, Cheol-Ho Park, Keunwan 2024-01-19T11:03:26Z 2024-01-19T11:03:26Z 2022-10-27 2022-09 1545-5963 https://pubs.kist.re.kr/handle/201004/114551 As the microbiome is composed of a variety of microbial interactions, it is imperative in microbiome research to identify a microbial sub-community that collectively conducts a specific function. However, current methodologies have been highly limited to analyzing conditional abundance changes of individual microorganisms without considering group-wise collective microbial features. To overcome this limitation, we developed a network-based method using nonnegative matrix factorization (NMF) to identify functional meta-microbial features (MMFs) that, as a group, better discriminate specific environmental conditions of samples using microbiome data. As proof of concept, large-scale human microbiome data collected from different body sites were used to identify body site-specific MMFs by applying NMF. The statistical test for MMFs led us to identify highly discriminative MMFs on sample classes, called synergistic MMFs (SYMMFs). Finally, we constructed a SYMMF-based microbial interaction network (SYMMF-net) by integrating all of the SYMMF information. Network analysis revealed core microbial modules closely related to critical sample properties. Similar results were also found when the method was applied to various disease-associated microbiome data. The developed method interprets high-dimensional microbiome data by identifying functional microbial modules on sample properties and intuitively representing their systematic relationships via a microbial network. English IEEE Computer Society Identification of Functional Microbial Modules Through Network-Based Analysis of Meta-Microbial Features Using Matrix Factorization Article 10.1109/TCBB.2021.3100893 1 IEEE/ACM Transactions on Computational Biology and Bioinformatics, v.19, no.5, pp.2851 - 2862 IEEE/ACM Transactions on Computational Biology and Bioinformatics 19 5 2851 2862 N scie scopus 000866522800028 2-s2.0-85112643092 Biochemical Research Methods Computer Science, Interdisciplinary Applications Mathematics, Interdisciplinary Applications Statistics & Probability Biochemistry & Molecular Biology Computer Science Mathematics Article LEVODOPA METABOLISM ABSORPTION DISEASE HEALTH GUT MICROBIOTA Sequential analysis Microorganisms Indexes Systematics Entropy Bioinformatics Taxonomy Microbiome meta-microbial feature microbial network analysis microbial module microbial community analysis microbiome modeling 16S rRNA sequencing