TY - JOUR
T1 - Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome
AU - Claussen, Jens Christian
AU - Skiecevičienė, Jurgita
AU - Wang, Jun
AU - Rausch, Philipp
AU - Karlsen, Tom H.
AU - Lieb, Wolfgang
AU - Baines, John F.
AU - Franke, Andre
AU - Hütt, Marc Thorsten
N1 - © 2017 Claussen et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
PY - 2017/6/22
Y1 - 2017/6/22
N2 - The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.
AB - The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.
UR - http://www.scopus.com/inward/record.url?scp=85021709459&partnerID=8YFLogxK
UR - http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005361
U2 - 10.1371/journal.pcbi.1005361
DO - 10.1371/journal.pcbi.1005361
M3 - Article
C2 - 28640804
AN - SCOPUS:85021709459
SN - 1553-734X
VL - 13
JO - PLoS computational biology
JF - PLoS computational biology
IS - 6
M1 - e1005361
ER -