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ISME J. 2016 Jul;10(7):1669-81. doi: 10.1038/ismej.2015.235. Epub 2016 Feb 23.

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.

Author information

1
Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO, USA.
2
BioFrontiers Institute, University of Colorado at Boulder, Boulder, CO, USA.
3
Department of Medicine, University of Colorado, Denver, CO, USA.
4
Department of Microbiology and Immunology, Rega Institute KU Leuven, Leuven, Belgium.
5
VIB Center for the Biology of Disease, VIB, Leuven, Belgium.
6
Laboratory of Microbiology, Vrije Universiteit Brussel, Brussels, Belgium.
7
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
8
CAS Key Laboratory of Environmental Biotechnology, Chinese Academy of Sciences, Beijing, China.
9
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA.
10
Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
11
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
12
Departments of Pediatrics, University of California San Diego, La Jolla, CA, USA.
13
Biota Technology, Inc., Denver, CO, USA.
14
Center for Microbiome Informatics and Therapeutics, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
15
Center for Computational Biology and Bioinformatics, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
16
Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
17
Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, USA.
18
Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
19
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.
20
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.

Abstract

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

PMID:
26905627
PMCID:
PMC4918442
DOI:
10.1038/ismej.2015.235
[Indexed for MEDLINE]
Free PMC Article

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