Balance Trees Reveal Microbial Niche Differentiation
James T. Morton,
Jon Sanders,
Robert A. Quinn,
Daniel McDonald,
Antonio Gonzalez,
Yoshiki Vázquez-Baeza,
Jose A. Navas-Molina,
Se Jin Song,
Jessica L. Metcalf,
Embriette R. Hyde,
Manuel Lladser,
Pieter C. Dorrestein,
Rob Knight
Affiliations
James T. Morton
Department of Pediatrics, University of California San Diego, La Jolla, California, USA
Jon Sanders
Department of Pediatrics, University of California San Diego, La Jolla, California, USA
Robert A. Quinn
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy, University of California San Diego, La Jolla, California, USA, and Department of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA
Daniel McDonald
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
Antonio Gonzalez
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
Yoshiki Vázquez-Baeza
Department of Pediatrics, University of California San Diego, La Jolla, California, USA
Jose A. Navas-Molina
Department of Pediatrics, University of California San Diego, La Jolla, California, USA
Se Jin Song
Department of Pediatrics, University of California San Diego, La Jolla, California, USA
Jessica L. Metcalf
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy, University of California San Diego, La Jolla, California, USA, and Department of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA
Embriette R. Hyde
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
Manuel Lladser
Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, USA
Pieter C. Dorrestein
Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
Rob Knight
Department of Pediatrics, University of California San Diego, La Jolla, California, USA
ABSTRACT Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss . Author Video: An author video summary of this article is available.
