Inference and Analysis of SPIEC-EASI Microbiome Networks

Henry W G Birt; Paul G Dennis

doi:10.1007/978-1-0716-1040-4_14

Inference and Analysis of SPIEC-EASI Microbiome Networks

Methods Mol Biol. 2021:2232:155-171. doi: 10.1007/978-1-0716-1040-4_14.

Authors

Henry W G Birt¹, Paul G Dennis²

Affiliations

¹ School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia.
² School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia. p.dennis@uq.net.au.

PMID: 33161547
DOI: 10.1007/978-1-0716-1040-4_14

Abstract

Network analysis facilitates examination of the interactions between different populations in a community. It can provide a range of metrics describing the social characteristics of each population and emergent structural properties of the community, which may be used to address novel ecological questions. Using a publicly available dataset, this chapter provides point-by-point code and instructions to infer and analyze a SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) network using free, open source software (R and Gephi).

Keywords: Amplicon; Covariance; Interactions; Microbiome; Networks; SPIEC-EASI.

MeSH terms

Algorithms
Computational Biology / methods*
Humans
Microbiota / genetics*
RNA, Ribosomal, 16S / genetics*
Software*

Substances

RNA, Ribosomal, 16S