Original Research ARTICLE
Linking associations of rare low-abundance species to their environments by association networks
- 1Genomic Medicine, University of Texas MD Anderson Cancer Center, United States
- 2Oak Ridge National Laboratory (DOE), United States
- 3School of Public Health, University of Texas Health Science Center, United States
- 4Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, United States
Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (Operational Taxonomic Units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.
Keywords: Metagenome, microbiome, Unsupervised analysis, Alpha and beta diversity, Sparse data, Anets, categorical data
Received: 20 Oct 2017;
Accepted: 08 Feb 2018.
Edited by:Michele Guindani, University of California, Irvine, United States
Reviewed by:Stephen Woloszynek, Drexel University, United States
Rohita Sinha, University of Nebraska-Lincoln, United States
Copyright: © 2018 Karpinets, Gopalakrishnan, Wargo, Futreal, Schadt and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Tatiana V. Karpinets, University of Texas MD Anderson Cancer Center, Genomic Medicine, Houston, United States, email@example.com