DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

Abstract

Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g., sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a two real data sets of 16S variable region amplicon sequences that were expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

Authors:
; ; ; ;
Publication Date:
Research Org.:
USDOE Joint Genome Institute (JGI), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDA
OSTI Identifier:
1501949
Alternate Identifier(s):
OSTI ID: 1904144
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Published Article
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Name: Frontiers in Microbiology Journal Volume: 10; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Media SA
Country of Publication:
Switzerland
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Microbiology; microbial community; correlation network; habitat filtering; network analysis algorithm; rhizosphere

Citation Formats

Brisson, Vanessa, Schmidt, Jennifer, Northen, Trent R., Vogel, John P., and Gaudin, Amélie. A New Method to Correct for Habitat Filtering in Microbial Correlation Networks. Switzerland: N. p., 2019. Web. doi:10.3389/fmicb.2019.00585.
Brisson, Vanessa, Schmidt, Jennifer, Northen, Trent R., Vogel, John P., & Gaudin, Amélie. A New Method to Correct for Habitat Filtering in Microbial Correlation Networks. Switzerland. https://doi.org/10.3389/fmicb.2019.00585
Brisson, Vanessa, Schmidt, Jennifer, Northen, Trent R., Vogel, John P., and Gaudin, Amélie. Wed . "A New Method to Correct for Habitat Filtering in Microbial Correlation Networks". Switzerland. https://doi.org/10.3389/fmicb.2019.00585.
@article{osti_1501949,
title = {A New Method to Correct for Habitat Filtering in Microbial Correlation Networks},
author = {Brisson, Vanessa and Schmidt, Jennifer and Northen, Trent R. and Vogel, John P. and Gaudin, Amélie},
abstractNote = {Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g., sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a two real data sets of 16S variable region amplicon sequences that were expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.},
doi = {10.3389/fmicb.2019.00585},
journal = {Frontiers in Microbiology},
number = ,
volume = 10,
place = {Switzerland},
year = {Wed Mar 20 00:00:00 EDT 2019},
month = {Wed Mar 20 00:00:00 EDT 2019}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.3389/fmicb.2019.00585

Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Molecular ecological network analyses
journal, January 2012


Determinants of community structure in the global plankton interactome
journal, May 2015


The century experiment: the first twenty years of UC Davis' Mediterranean agroecological experiment
journal, January 2018

  • Wolf, Kristina M.; Torbert, Emma E.; Bryant, Dennis
  • Ecology, Vol. 99, Issue 2
  • DOI: 10.1002/ecy.2105

Detecting Novel Associations in Large Data Sets
journal, December 2011


Evolution of the Crop Rhizosphere: Impact of Domestication on Root Exudates in Tetraploid Wheat (Triticum turgidum L.)
journal, December 2017

  • Iannucci, Anna; Fragasso, Mariagiovanna; Beleggia, Romina
  • Frontiers in Plant Science, Vol. 8
  • DOI: 10.3389/fpls.2017.02124

CoNet app: inference of biological association networks using Cytoscape
journal, January 2016


Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions
journal, September 2010

  • Ovaskainen, Otso; Hottola, Jenni; Siitonen, Juha
  • Ecology, Vol. 91, Issue 9
  • DOI: 10.1890/10-0173.1

Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly
journal, March 2018


Using network analysis to explore co-occurrence patterns in soil microbial communities
journal, September 2011

  • Barberán, Albert; Bates, Scott T.; Casamayor, Emilio O.
  • The ISME Journal, Vol. 6, Issue 2
  • DOI: 10.1038/ismej.2011.119

Deciphering microbial interactions and detecting keystone species with co-occurrence networks
journal, May 2014


Soil bacterial community composition altered by increased nutrient availability in Arctic tundra soils
journal, October 2014

  • Koyama, Akihiro; Wallenstein, Matthew D.; Simpson, Rodney T.
  • Frontiers in Microbiology, Vol. 5
  • DOI: 10.3389/fmicb.2014.00516

DADA2: High-resolution sample inference from Illumina amplicon data
journal, May 2016

  • Callahan, Benjamin J.; McMurdie, Paul J.; Rosen, Michael J.
  • Nature Methods, Vol. 13, Issue 7
  • DOI: 10.1038/nmeth.3869

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
journal, February 2016

  • Weiss, Sophie; Van Treuren, Will; Lozupone, Catherine
  • The ISME Journal, Vol. 10, Issue 7
  • DOI: 10.1038/ismej.2015.235

Inferring Correlation Networks from Genomic Survey Data
journal, September 2012


Best practices for analysing microbiomes
journal, May 2018


Sparse and Compositionally Robust Inference of Microbial Ecological Networks
journal, May 2015

  • Kurtz, Zachary D.; Müller, Christian L.; Miraldi, Emily R.
  • PLOS Computational Biology, Vol. 11, Issue 5
  • DOI: 10.1371/journal.pcbi.1004226

Contributions of Zea mays subspecies mexicana haplotypes to modern maize
journal, November 2017


A global atlas of the dominant bacteria found in soil
journal, January 2018

  • Delgado-Baquerizo, Manuel; Oliverio, Angela M.; Brewer, Tess E.
  • Science, Vol. 359, Issue 6373
  • DOI: 10.1126/science.aap9516

Co-occurrence Analysis of Microbial Taxa in the Atlantic Ocean Reveals High Connectivity in the Free-Living Bacterioplankton
journal, May 2016


A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system
journal, June 2012

  • Barillot, Cindy D. C.; Sarde, Claude-Olivier; Bert, Valerie
  • Annals of Microbiology, Vol. 63, Issue 2
  • DOI: 10.1007/s13213-012-0491-y

The interconnected rhizosphere: High network complexity dominates rhizosphere assemblages
journal, June 2016

  • Shi, Shengjing; Nuccio, Erin E.; Shi, Zhou J.
  • Ecology Letters, Vol. 19, Issue 8
  • DOI: 10.1111/ele.12630

From hairballs to hypotheses–biological insights from microbial networks
journal, July 2018

  • Röttjers, Lisa; Faust, Karoline
  • FEMS Microbiology Reviews, Vol. 42, Issue 6
  • DOI: 10.1093/femsre/fuy030

Microbial Co-occurrence Relationships in the Human Microbiome
journal, July 2012


Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
journal, December 2014


phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
journal, April 2013


Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters
journal, March 2017


Drought and host selection influence bacterial community dynamics in the grass root microbiome
journal, July 2017

  • Naylor, Dan; DeGraaf, Stephanie; Purdom, Elizabeth
  • The ISME Journal, Vol. 11, Issue 12
  • DOI: 10.1038/ismej.2017.118