Modeling association in microbial communities with clique loglinear models

Dobra, Adrian; Valdes, Camilo; Ajdić, Dragana; Clarke, Bertrand; Clarke, Jennifer

doi:10.1214/18-aoas1229

Cited by 4 publications

(3 citation statements)

References 64 publications

(70 reference statements)

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“…Tools such as FLINT ( Valdes et al, 2019 ) and Kraken 2 ( Wood et al, 2019 ) facilitate the creation of microbial abundance profiles either from metagenomic whole-genome DNA sequencing (mWGS) or 16S-amplicon sequencing (16S) data. These abundance profiles are the stepping stones for downstream analyses such as differential abundance studies ( White et al, 2009 ), co-occurrence pattern discovery ( Dutilh et al, 2014 ; Fernandez et al, 2015 ; Fernandez et al, 2016 ; Weiss et al, 2016 ), Bayesian analyses ( Rahman Sazal et al, 2018 ; Adrian et al, 2019 ), biomarker identification ( Segata et al, 2011 ), multi-omics analyses ( IHMP Consortium, 2014 ; Aguiar-Pulido et al, 2016 ), and analyses of profiles from longitudinal studies ( Jose et al, 2019 ; Ruiz-Perez et al, 2019 ). Lower sequencing costs have resulted in an increasing number of larger deep sequencing metagenomic data sets ( Muir et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Microbiome maps: Hilbert curve visualizations of metagenomic profiles

et al. 2023

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Abundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. We created Jasper, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order taxa in multiple ways, and the resulting microbiome maps can highlight “hot spots” of microbes that are dominant in taxonomic clades or biological conditions. We use Jasper to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes.

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Section: Introductionmentioning

confidence: 99%

Microbiome maps: Hilbert curve visualizations of metagenomic profiles

et al. 2023

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“…Tools such as Flint [36] and Kraken 2 [40] facilitate the process to create detailed microbial abundance profiles for tens of thousands of genomes for both metagenomic whole-genome DNA sequencing (mWGS) as well as 16S-amplicon sequencing (16S). The abundance profiles are the stepping stones for downstream analyses such as differential abundance studies [38], co-occurrence pattern discovery [20, 22, 37, 21], Bayesian analyses [19, 34], biomarker identification [35], multi-omics analyses [8, 9], and analyses of profiles from longitudinal studies [28, 33]. Low-cost sequencing has meant that an increasing number of metagenomic data sets are being generated [29].…”

Section: Introductionmentioning

confidence: 99%

Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

Valdes

Stebliankin

Ruiz-Perez

et al. 2021

Preprint

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MotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent is complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number in the thousands. We present a technique for succinct visualization of abundance profiles using a space-filling curve that transforms a profile into an interpretable 2D image.ResultsJasper is a tool for visualizing profiles from metagenomic whole-genome sequencing and 16S, and orders taxa along a space-filling Hilbert curve. The result is a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order the taxa in one of two ways, and depending on the ordering, the microbiome maps can highlight “hot spots” of microbes that are either dominant in taxonomic clades or to the biological conditions under study.We use Jasper to visualize samples from the Human Microbiome Project and from a Chronic Kidney Disease study, and discuss a variety of ways in which the microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can be converted into animated movies that bring to life the dynamicity of microbiomes.AvailabilityJasper will be available as free software from the Mac App Store and biorg.cs.fiu.edu/jasperSupplementary informationSupplementary materials are available at biorg.cs.fiu.edu/jasper

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“…The importance of the human microbiome has been increasingly recognized in biomedicine, due to its association with many complex diseases, such as obesity (Turnbaugh et al, 2009), cardiovascular disease (Koeth et al, 2013), diabetes (Qin et al, 2012;Dobra et al, 2019;Ren et al, 2020), liver cirrhosis (Qin et al, 2014), inflammatory bowel disease (Halfvarson et al, 2017), psoriasis (Tett et al, 2017), and colorectal cancer (Zackular et al, 2016), and its response to cancer immunotherapy (Frankel et al, 2017;Gopalakrishnan et al, 2018;Zitvogel et al, 2018). Advances in high-throughput next generation sequencing technologies (e.g., 16S ribosomal RNA [rRNA] sequencing, shotgun sequencing) make it possible to fully characterize the human microbiome, better understand the risk factors (e.g., clinical, genetic, environmental) that shape the human microbiome, and decipher the function and impact of the microbiome profile on human health and diseases (Li, 2015;Chen and Li, 2016;Zhu et al, 2017;Zhang et al, 2018;Reyes-Gibby et al, 2020;Sun et al, 2020;Wang et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Inverse Probability Weighting-based Mediation Analysis for Microbiome Data

Zhang¹,

Wang²,

Shen³

et al. 2021

Preprint

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Mediation analysis is an important tool to study casual associations in biomedical and other scientific areas and has recently gained attention in microbiome studies. With a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy intensity levels on the infection status is mediated by the microbial taxa abundance. The unique characteristics of the microbial mediators-high-dimensionality, zeroinflation, and dependence-call for new methodological developments in mediation analysis. The presence of an exposure-induced mediator-outcome confounder, antibiotics usage, further requires a delicate treatment in the analysis. To address these unique challenges brought by our motivating microbiome study, we propose a novel nonparametric identification formula for the interventional indirect effect (IIE), a measure recently developed for studying mediation effects. We develop the corresponding estimation algorithm and test the presence of mediation effects via constructing the nonparametric bias-corrected and accelerated bootstrap confidence *

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Modeling association in microbial communities with clique loglinear models

Cited by 4 publications

References 64 publications

Microbiome maps: Hilbert curve visualizations of metagenomic profiles

Microbiome maps: Hilbert curve visualizations of metagenomic profiles

Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

Inverse Probability Weighting-based Mediation Analysis for Microbiome Data

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