CCLasso: correlation inference for compositional data through Lasso

Fang, Huaying; Huang, Chengcheng; Zhao, Hongyu; Deng, Minghua

doi:10.1093/bioinformatics/btv349

Cited by 164 publications

(156 citation statements)

References 24 publications

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“…Correlation inference for Compositional data through Lasso (CCLasso) was used to infer the correlation network for latent variables of taxonomic data (Fang et al, 2015). Before the network construction, ALDex2 package was used to identify archaeal OTUs or phototroph OTUs having significant changes over the sampling period.…”

Section: Discussionmentioning

confidence: 99%

Localized high abundance of Marine Group II archaea in the subtropical Pearl River Estuary: implications for their niche adaptation

Xie

Luo

Murugapiran

et al. 2017

Environmental Microbiology

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Marine Group II archaea are widely distributed in global oceans and dominate the total archaeal community within the upper euphotic zone of temperate waters. However, factors controlling the distribution of MGII are poorly delineated and the physiology and ecological functions of these still-uncultured organisms remain elusive. In this study, we investigated the planktonic MGII associated with particles and in free-living forms in the Pearl River Estuary (PRE) over a 10-month period. We detected high abundance of particle-associated MGII in PRE (up to ∼10 16S rRNA gene copies/l), which was around 10-fold higher than the free-living MGII in the same region, and an order of magnitude higher than previously reported in other marine environments. 10‰ salinity appeared to be a threshold value for these MGII because MGII abundance decreased sharply below it. Above 10‰ salinity, the abundance of MGII on the particles was positively correlated with phototrophs and MGII in the surface water was negatively correlated with irradiance. However, the abundances of those free-living MGII showed positive correlations with salinity and temperature, suggesting the different physiological characteristics between particle-attached and free-living MGIIs. A nearly completely assembled metagenome, MGIIa_P, was recovered using metagenome binning methods. Compared with the other two MGII genomes from surface ocean, MGIIa_P contained higher proportions of glycoside hydrolases, indicating the ability of MGIIa_P to hydrolyse glycosidic bonds in complex sugars in PRE. MGIIa_P is the first assembled MGII metagenome containing a catalase gene, which might be involved in scavenging reactive oxygen species generated by the abundant phototrophs in the eutrophic PRE. Our study presented the widespread and high abundance of MGII in the water columns of PRE, and characterized the determinant abiotic factors affecting their distribution. Their association with heterotrophs, preference for particles and resourceful metabolic traits indicate MGII might play a significant role in metabolising organic matters in the PRE and other temperate estuarine systems.

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Section: Discussionmentioning

confidence: 99%

Localized high abundance of Marine Group II archaea in the subtropical Pearl River Estuary: implications for their niche adaptation

Xie

Luo

Murugapiran

et al. 2017

Environmental Microbiology

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“…CCLasso (Correlation inference for Compositional data through Lasso) is yet another method developed to infer correlations from compositional data [68]. CCLasso uses least squares with L1 penalty after log ratio transformation for raw compositional data to infer the correlations among microbes through a latent variable model.…”

Section: Rebacca ([ 7 _ T D $ D I F F ] Regularized [ 8 _ T D $ D I Fmentioning

confidence: 99%

Disentangling Interactions in the Microbiome: A Network Perspective

Layeghifard

Hwang

Guttman

2017

Trends in Microbiology

641

431

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Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

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“…We compare the performance of the HARMONIES and several widely used methods for inferring microbiome networks. These methods include SPIEC-EASI Kurtz et al (2015), CClasso Fang et al (2015) and correlation-based network estimation used in Faust and Raes (2016); Weiss et al (2016) . While the proposed model and SPIEC-EASI infer the network structure from sparse precision matrices, CClasso, and the correlation-based method utilize sparse correlation matrices to represent the network.…”

Section: Simulation Scenariosmentioning

confidence: 99%

HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity

Jiang

Xiao

Koh

et al. 2020

Preprint

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The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to construct microbial networks from the microbiome sequencing count data. As microbiome count data are high-dimensional and suffer from uneven sampling depth, over-dispersion, and zeroinflation, these characteristics can bias the network estimation and require specialized analytical tools. Here we propose a general framework, HARMONIES, a Hybrid Approach foR MicrobiOme Network Inferences via Exploiting Sparsity, to infer a sparse microbiome network. HARMONIES first utilizes a zero-inflated negative binomial (ZINB) distribution to model the skewness and excess zeros in the microbiome data, as well as incorporates a stochastic process prior for sample-wise normalization. This approach infers a sparse and stable network by imposing non-trivial regularizations based on the Gaussian graphical model. In comprehensive simulation studies, HARMONIES outperformed four

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CCLasso: correlation inference for compositional data through Lasso

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 164 publications

References 24 publications

Localized high abundance of Marine Group II archaea in the subtropical Pearl River Estuary: implications for their niche adaptation

Localized high abundance of Marine Group II archaea in the subtropical Pearl River Estuary: implications for their niche adaptation

Disentangling Interactions in the Microbiome: A Network Perspective

HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity

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