Sparse and Compositionally Robust Inference of Microbial Ecological Networks

By Zachary D. Kurtz, Christian L. Müller, Emily R. Miraldi, Dan R. Littman, Martin J. Blaser, Richard Bonneau. PLoS Comput. Biol., 2014

1,531 citations recorded by the source corpus.

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/af741719135cd124243ce296ac52c87758fda71f

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Citations in the corpus

Network analysis methods for studying microbial communities: A mini review (2021, methodology): “Two different strategies are implemented in SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) [42] after applying a CLR transformation to the data to address compositionality.”
ggClusterNet: An R package for microbiome network analysis and modularity‐based multiple network layouts (2022, methodology): “Tools for network analysis of microbiome included web tool MENA [11] (MENAP), R packages (WGCNA [12], igraph [13], ggraph [14], SpiecEasi [15], interactive software (Cytoscape [16] and Gephi [17]), python packages (Net-workX [18] and SparCC [19]), and so forth.”
Disentangling Interactions in the Microbiome: A Network Perspective (2016, methodology): “SPIEC-EASI has been used to predict previously unknown microbial associations using data from the American Gut project [61].”
LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, background): “However, in practice, taxa are interconnected forming networks [48].”
A practical guide to amplicon and metagenomic analysis of microbiome data (2020, methodology): “Correlation coef ﬁ cients and signi ﬁ cant P -values could be computed using the cor.test() function in R or more robust tools that are suitable for compositional data such as the SparCC (sparse correlations for compositional data) package (Kurtz et al., 2015).”
NetCoMi: network construction and comparison for microbiome data in R (2020, neutral): “Conditional dependence expresses the relation between two variables conditioned on all other variables in the data set [28].”
Rhizosphere-associated bacterial network structure and spatial distribution differ significantly from bulk soil in wheat crop fields (2017, result): “Spiec-Easi (Sparse InversE Covariance Estimation for Ecological Association Inference) network analysis in both OTU level and class level (Kurtz et al., 2015; Cardona et al., 2016) which supported the mutual corroboration carried out in the R environment (http://www.r-project.org/) with “SpiecEasi”…”
Microbiome Datasets Are Compositional: And This Is Not Optional (2017, background): “There are several more rigorous approaches that can be applied to analyze correlation in microbiome datasets, including SPARCC (Friedman and Alm, 2012) and SPieCeasi (Kurtz et al., 2015), both of which assume a sparse data matrix, and the φ (Lovell et al., 2015) and ρ (Erb andNotredame, 2016)metrics”
Regression Analysis for Microbiome Compositional Data (2016, methodology): “CLR has been shown to be effective in transforming compositional data to approximately multivariate normal in many real compositional and microbiome data [Aitchison (1982), Kurtz et al. (2015)].”