Detection of multi-dimensional co-exclusion patterns in microbial communities

Bioinformatics. 2018 Nov 1;34(21):3695-3701. doi: 10.1093/bioinformatics/bty414.


Motivation: Identification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting micro-organisms' intolerance to each other's presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond micro-organism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.

Results: The implemented computational pipeline (CoEx) tested against 2380 microbial profiles (samples) from The Human Microbiome Project resulted in body-site specific pairwise co-exclusion patterns.

Availability and implementation: C++ source code for calculation of the score and P-value for two, three and four dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Microbiota*
  • Software