NFnetFu: a novel workflow for microbiome data fusion
Abstract
Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.
Citations
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Date
2021-06-08
Type
Article
Subject
Oncology. Pathology., Microbiology. Immunology
Collections
Citation
Bisht V, Acharjee A, Gkoutos GV. NFnetFu: A novel workflow for microbiome data fusion. Comput Biol Med. 2021 Aug;135:104556. doi: 10.1016/j.compbiomed.2021.104556. Epub 2021 Jun 8
Journal / Source Title
Computers in Biology and Medicine
DOI
10.1016/j.compbiomed.2021.104556
PMID
34216888
Publisher
Elsevier
Publisher’s URL
http://www.sciencedirect.com/science/journal/00104825