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

By Sophie J. Weiss, William W. Van Treuren, Catherine A. Lozupone, Karoline Faust, Jonathan Friedman, Ye Deng, L. Xia, Zhenjiang Xu, L. Ursell, Eric J. Alm and 7 more. The ISME Journal, 2016

697 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b5b98b62cf6ce1a4831217f3e70b63750e0220fb

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In Biomedical Engineering

David B. Dunson

In Molecular Biology

Citations in the corpus

Biogeographic patterns of microbial association networks in paddy soil within Eastern China (2020, background): “Despite lack of empirical evidence, microbial species with mutualistic relationships are assumed to positively associate with each other, while antagonistic microbes such as competition or predation may negatively associate (Weiss et al., 2016).”
How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, background): “Here, much of the focus has been on inferring association networks from sequence count data (Steele et al. 2011; Weiss et al. 2016), and on asking how such association networks relate to environmental conditions or ecosystem processes (e.”
Linking metagenomics to aquatic microbial ecology and biogeochemical cycles (2019, background): “Although correlation-based models suffer from limitations (Weiss et al. 2016; Coenen and Weitz 2018; Hirano and Takemoto 2019), not least from the challenges of working with relative abundance data, they are often powerful in their simplicity and provide opportunities to generate hypotheses about…”
Disentangling Interactions in the Microbiome: A Network Perspective (2016, positive): “[25] evaluated the performance of eight correlation methods on both synthetic and real data in response to challenges specific to microbiome studies and assessed their ability to distinguish signals from noise and detect a range of ecological and time-series relationships.”
Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “Unfortunately, the effect cannot be diluted away as has been recommended (Weiss et al., 2016).”
Network analysis methods for studying microbial communities: A mini review (2021, background): “SparCC was shown to be better suited to avoid spurious correlations compared to direct Pearson correlations [15] at the cost of higher computational complexity [30].”
NetCoMi: network construction and comparison for microbiome data in R (2020, neutral): “Comparative studies often concentrate on the performance in edge recovery, for instance, via precision-recall curves [28, 55, 115, 116] or distances between true and estimated associations [27, 28, 53, 55].”
Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming (2018, background): “, niche-overlap), and therefore, do not necessarily reflect direct interactions between taxa [33, 69].”
CoNet app: inference of biological association networks using Cytoscape (2016, neutral): “CoNet was developed to carry out microbial network inference, but its generic design makes it applicable to any data set where objects have been observed repeatedly.”