Compositional data network analysis via lasso penalized D-trace loss

2019 ◽  
Vol 35 (18) ◽  
pp. 3404-3411 ◽  
Author(s):  
Huili Yuan ◽  
Shun He ◽  
Minghua Deng
Keyword(s):  
Microbial Communities ◽  
Compositional Data ◽  
Sparse Matrix ◽  
Mouse Skin ◽  
Direct Interaction ◽  
Interaction Network ◽  
Gene Profiling ◽  
Supplementary Information ◽  
Rrna Gene ◽  
Skin Microbiome

Abstract Motivation With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial communities helps in the identification of mechanisms underlying community structure. However, the analysis of compositional data remains challenging by the relative information conveyed by such data, as well as its high dimensionality. Results In this article, we first propose a novel loss function for compositional data called CD-trace based on D-trace loss. A sparse matrix estimator for the direct interaction network is defined as the minimizer of lasso penalized CD-trace loss under positive-definite constraint. An efficient alternating direction algorithm is developed for numerical computation. Simulation results show that CD-trace compares favorably to gCoda and that it is better than sparse inverse covariance estimation for ecological association inference (SPIEC-EASI) (hereinafter S-E) in network recovery with compositional data. Finally, we test CD-trace and compare it to the other methods noted above using mouse skin microbiome data. Availability and implementation The CD-trace is open source and freely available from https://github.com/coamo2/CD-trace under GNU LGPL v3. Supplementary information Supplementary data are available at Bioinformatics online.

Direct interaction network inference for compositional data via codaloss

2020 ◽  
Vol 18 (06) ◽  
pp. 2050037
Author(s):  
Liang Chen ◽  
Shun He ◽  
Yuyao Zhai ◽  
Minghua Deng
Keyword(s):  
Microbial Communities ◽  
Network Inference ◽  
Compositional Data ◽  
Direct Interaction ◽  
Interaction Network ◽  
16S Rrna Gene Sequencing ◽  
Underlying Mechanism ◽  
Rrna Gene ◽  
Rrna Gene Sequencing ◽  
Microbiome Data

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss .

scholarly journals A Framework to Incorporate D-trace Loss into Compositional Data Analysis

2018 ◽  
Author(s):  
Shun He ◽  
Minghua Deng
Keyword(s):  
Data Analysis ◽  
Compositional Data ◽  
Direct Interaction ◽  
Interaction Network ◽  
High Dimensionality ◽  
Compositional Data Analysis ◽  
Skin Microbiome ◽  
Biological Studies ◽  
Differential Network ◽  
Network Estimation

AbstractThe development of high-throughput sequencing technologies for 16S rRNA gene profiling provides higher quality compositional data for microbe communities. Inferring the direct interaction network under a specific condition and understanding how the network structure changes between two different environmental or genetic conditions are two important topics in biological studies. However, the compositional nature and high dimensionality of the data are challenging in the context of network and differential network recovery. To address this problem in the present paper, we proposed a framework to incorporate the data transformations developed for compositional data analysis into D-trace loss for network and differential network estimation, respectively. The sparse matrix estimators are defined as the minimizer of the corresponding lasso penalized loss. This framework is characterized by its straightforward application based on the ADMM algorithm for numerical solution. Simulations show that the proposed method outperforms other state-of-the-art methods in network and differential network inference under different scenarios. Finally, as an illustration, our method is applied to a mouse skin microbiome data.Author summaryInferring the direct interactions among microbes and how these interactions change under different conditions are important to understand community-wide dynamics. The compositional nature and high dimensionality are two distinctive features of microbial data, which invalidate traditional correlation analysis and challenge interaction network estimation. In this study, we set up a framework that combines data transformation with D-trace loss to infer the direct interaction network and differential network from compositional data. Simulations and real data analysis show that our proposed methods lead to results with higher accuracy and stability.

ClusFCM: AN ALGORITHM FOR PREDICTING PROTEIN FUNCTIONS USING HOMOLOGIES AND PROTEIN INTERACTIONS

2008 ◽  
Vol 06 (01) ◽  
pp. 203-222 ◽  
Author(s):  
CAO NGUYEN ◽  
MICHAEL MANNINO ◽  
KATHELEEN GARDINER ◽  
KRZYSZTOF J. CIOS
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Direct Interaction ◽  
Interaction Network ◽  
Roc Curves ◽  
Fuzzy Cognitive Maps ◽  
Supplementary Information ◽  
Protein Interaction Data ◽  
Protein Functions ◽  
Grid Database

We introduce a new algorithm, called ClusFCM, which combines techniques of clustering and fuzzy cognitive maps (FCM) for prediction of protein functions. ClusFCM takes advantage of protein homologies and protein interaction network topology to improve low recall predictions associated with existing prediction methods. ClusFCM exploits the fact that proteins of known function tend to cluster together and deduce functions not only through their direct interaction with other proteins, but also from other proteins in the network. We use ClusFCM to annotate protein functions for Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), and Drosophila melanogaster (fly) using protein–protein interaction data from the General Repository for Interaction Datasets (GRID) database and functional labels from Gene Ontology (GO) terms. The algorithm's performance is compared with four state-of-the-art methods for function prediction — Majority, χ2 statistics, Markov random field (MRF), and FunctionalFlow — using measures of Matthews correlation coefficient, harmonic mean, and area under the receiver operating characteristic (ROC) curves. The results indicate that ClusFCM predicts protein functions with high recall while not lowering precision. Supplementary information is available at .

scholarly journals Effects of the Denitrification Inhibitor “Procyanidins” on the Diversity, Interactions, and Potential Functions of Rhizosphere-Associated Microbiome

2021 ◽  
Vol 9 (7) ◽  
pp. 1406
Author(s):  
Mylène Hugoni ◽  
William Galland ◽  
Solène Lecomte ◽  
Maxime Bruto ◽  
Mohamed Barakat ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Plant Biomass ◽  
Plant Secondary Metabolites ◽  
Interaction Network ◽  
Microbial Interactions ◽  
Rrna Gene ◽  
Loamy Sand Soil ◽  
Loam Soil ◽  
Functional Inference ◽  
Edaphic Variables

Some plant secondary metabolites, such as procyanidins, have been demonstrated to cause biological denitrification inhibition (BDI) of denitrifiers in soils concomitantly with a gain in plant biomass. The present work evaluated whether procyanidins had an impact on the diversity of nontarget microbial communities that are probably involved in soil fertility and ecosystem services. Lettuce plants were grown in two contrasting soils, namely Manziat (a loamy sand soil) and Serail (a sandy clay loam soil) with and without procyanidin amendment. Microbial diversity was assessed using Illumina sequencing of prokaryotic 16S rRNA gene and fungal ITS regions. We used a functional inference to evaluate the putative microbial functions present in both soils and reconstructed the microbial interaction network. The results showed a segregation of soil microbiomes present in Serail and Manziat that were dependent on specific soil edaphic variables. For example, Deltaproteobacteria was related to total nitrogen content in Manziat, while Leotiomycetes and Firmicutes were linked to Ca2+ in Serail. Procyanidin amendment did not affect the diversity and putative activity of microbial communities. In contrast, microbial interactions differed according to procyanidin amendment, with the results showing an enrichment of Entotheonellaeota and Mucoromycota in Serail soil and of Dependentiae and Rozellomycetes in Manziat soil.

scholarly journals The Sputum Microbiome in Pulmonary Tuberculosis and Its Association With Disease Manifestations: A Cross-Sectional Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Monica R. Ticlla ◽  
Jerry Hella ◽  
Hellen Hiza ◽  
Mohamed Sasamalo ◽  
Francis Mhimbira ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Compositional Data ◽  
Community Diversity ◽  
Compositional Data Analysis ◽  
Rrna Gene ◽  
X Rays ◽  
Microbial Composition ◽  
Pulmonary Tb ◽  
Airway Microbiome ◽  
Microbial Associations

Each day, approximately 27,000 people become ill with tuberculosis (TB), and 4,000 die from this disease. Pulmonary TB is the main clinical form of TB, and affects the lungs with a considerably heterogeneous manifestation among patients. Immunomodulation by an interplay of host-, environment-, and pathogen-associated factors partially explains such heterogeneity. Microbial communities residing in the host's airways have immunomodulatory effects, but it is unclear if the inter-individual variability of these microbial communities is associated with the heterogeneity of pulmonary TB. Here, we investigated this possibility by characterizing the microbial composition in the sputum of 334 TB patients from Tanzania, and by assessing its association with three aspects of disease manifestations: sputum mycobacterial load, severe clinical findings, and chest x-ray (CXR) findings. Compositional data analysis of taxonomic profiles based on 16S-rRNA gene amplicon sequencing and on whole metagenome shotgun sequencing, and graph-based inference of microbial associations revealed that the airway microbiome of TB patients was shaped by inverse relationships between Streptococcus and two anaerobes: Selenomonas and Fusobacterium. Specifically, the strength of these microbial associations was negatively correlated with Faith's phylogenetic diversity (PD) and with the accumulation of transient genera. Furthermore, low body mass index (BMI) determined the association between abnormal CXRs and community diversity and composition. These associations were mediated by increased abundance of Selenomonas and Fusobacterium, relative to the abundance of Streptococcus, in underweight patients with lung parenchymal infiltrates and in comparison to those with normal chest x-rays. And last, the detection of herpesviruses and anelloviruses in sputum microbial assemblage was linked to co-infection with HIV. Given the anaerobic metabolism of Selenomonas and Fusobacterium, and the hypoxic environment of lung infiltrates, our results suggest that in underweight TB patients, lung tissue remodeling toward anaerobic conditions favors the growth of Selenomonas and Fusobacterium at the expense of Streptococcus. These new insights into the interplay among particular members of the airway microbiome, BMI, and lung parenchymal lesions in TB patients, add a new dimension to the long-known association between low BMI and pulmonary TB. Our results also drive attention to the airways virome in the context of HIV-TB coinfection.

scholarly journals Skin bacterial communities of neotropical treefrogs vary with local environmental conditions at the time of sampling

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7044 ◽  
Author(s):  
Angie Estrada ◽  
Myra C. Hughey ◽  
Daniel Medina ◽  
Eria A. Rebollar ◽  
Jenifer B. Walke ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Temporal Variation ◽  
Microbial Communities ◽  
Relative Abundance ◽  
Bacterial Communities ◽  
Community Diversity ◽  
Amphibian Conservation ◽  
Rrna Gene ◽  
Skin Microbiome ◽  
Amphibian Skin

The amphibian skin microbiome has been the focus of recent studies aiming to better understand the role of these microbial symbionts in host defense against disease. However, host-associated microbial communities are complex and dynamic, and changes in their composition and structure can influence their function. Understanding temporal variation of bacterial communities on amphibian skin is critical for establishing baselines from which to improve the development of mitigation techniques based on probiotic therapy and provides long-term host protection in a changing environment. Here, we investigated whether microbial communities on amphibian skin change over time at a single site. To examine this, we collected skin swabs from two pond-breeding species of treefrogs, Agalychnis callidryas and Dendropsophus ebraccatus, over 4 years at a single lowland tropical pond in Panamá. Relative abundance of operational taxonomic units (OTUs) based on 16S rRNA gene amplicon sequencing was used to determine bacterial community diversity on the skin of both treefrog species. We found significant variation in bacterial community structure across long and short-term time scales. Skin bacterial communities differed across years on both species and between seasons and sampling days only in D. ebraccatus. Importantly, bacterial community structures across days were as variable as year level comparisons. The differences in bacterial community were driven primarily by differences in relative abundance of key OTUs and explained by rainfall at the time of sampling. These findings suggest that skin-associated microbiomes are highly variable across time, and that for tropical lowland sites, rainfall is a good predictor of variability. However, more research is necessary to elucidate the significance of temporal variation in bacterial skin communities and their maintenance for amphibian conservation efforts.

scholarly journals Evaluation of the Effect of Storage Methods on Fecal, Saliva, and Skin Microbiome Composition

mSystems ◽  
2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Clarisse Marotz ◽  
Kellen J. Cavagnero ◽  
Se Jin Song ◽  
Daniel McDonald ◽  
Stephen Wandro ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Room Temperature ◽  
Cost Effective ◽  
Rrna Gene ◽  
Sequencing Analysis ◽  
Skin Microbiome ◽  
Metagenomic Sequencing ◽  
Sample Storage ◽  
Microbial Composition ◽  
Optimal Ratio

ABSTRACT As the number of human microbiome studies expand, it is increasingly important to identify cost-effective, practical preservatives that allow for room temperature sample storage. Here, we reanalyzed 16S rRNA gene amplicon sequencing data from a large sample storage study published in 2016 and performed shotgun metagenomic sequencing on remnant DNA from this experiment. Both results support the initial findings that 95% ethanol, a nontoxic, cost-effective preservative, is effective at preserving samples at room temperature for weeks. We expanded on this analysis by collecting a new set of fecal, saliva, and skin samples to determine the optimal ratio of 95% ethanol to sample. We identified optimal collection protocols for fecal samples (storing a fecal swab in 95% ethanol) and saliva samples (storing unstimulated saliva in 95% ethanol at a ratio of 1:2). Storing skin swabs in 95% ethanol reduced microbial biomass and disrupted community composition, highlighting the difficulties of low biomass sample preservation. The results from this study identify practical solutions for large-scale analyses of fecal and oral microbial communities. IMPORTANCE Expanding our knowledge of microbial communities across diverse environments includes collecting samples in places far from the laboratory. Identifying cost-effective preservatives that will enable room temperature storage of microbial communities for sequencing analysis is crucial to enabling microbiome analyses across diverse populations. Here, we validate findings that 95% ethanol efficiently preserves microbial composition at room temperature for weeks. We also identified the optimal ratio of 95% ethanol to sample for stool and saliva to preserve both microbial load and composition. These results provide rationale for an accessible, nontoxic, cost-effective solution that will enable crowdsourcing microbiome studies, such as The Microsetta Initiative, and lower the barrier for collecting diverse samples.

scholarly journals A Comparative Study on the Microbial Communities of Rhynchophorus ferrugineus (Red Palm Weevil)-Infected and Healthy Palm Trees

2021 ◽  
Author(s):  
N. Alshammari ◽  
Meshari Alazmi ◽  
Naimah A. Alanazi ◽  
Abdel Moneim E. Sulieman ◽  
Vajid N. Veettil ◽  
...  
Keyword(s):  
Microbial Communities ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rhynchophorus Ferrugineus ◽  
Tree Trunk ◽  
Red Palm Weevil ◽  
Healthy Tree ◽  
Palm Trees ◽  
Infected Tree ◽  
Palm Weevil

AbstractSeveral studies have investigated palm trees’ microbiota infected with red palm weevil (RPW) (Rhynchophorus ferrugineus), the major pest of palm trees. This study compared the microbial communities of infected and uninfected palm trees in the Hail region, Northern Saudi Arabia, determined by high-throughput 16S rRNA gene sequencing by Illumina MiSeq. The results indicated that taxonomic diversity variation was higher for infected tree trunk than the healthy tree trunk. Soil samples from the vicinity of healthy and infected trees did not have a significant variation in bacterial diversity. Myxococcota, Acidobacteriota, and Firmicutes were the dominant phyla in RPW-infected tree trunk, and Pseudomonadaceae was the most prominent family. This study is the first report on the characterization of RPW-infected and healthy palm trees’ microbiome.

scholarly journals Effect of a bioconverted product of Lotus corniculatus seed on the axillary microbiome and body odor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Min-Ji Kim ◽  
Setu Bazie Tagele ◽  
HyungWoo Jo ◽  
Min-Chul Kim ◽  
YeonGyun Jung ◽  
...  
Keyword(s):  
Volatile Fatty Acids ◽  
Profile Analysis ◽  
16S Rrna Gene Sequencing ◽  
Lotus Corniculatus ◽  
Biologically Active ◽  
Rrna Gene ◽  
Skin Microbiome ◽  
Body Odor ◽  
Rrna Gene Sequencing ◽  
Study Participants

AbstractThe skin microbiome, especially the axillary microbiome, consists of odor-causing bacteria that decompose odorless sweat into malodor compounds, which contributes to the formation of body odor. Plant-derived products are a cheap source of bioactive compounds that are common ingredients in cosmetics. Microbial bioconversion of natural products is an ecofriendly and economical method for production of new or improved biologically active compounds. Therefore, in this study, we tested the potential of a Lactobacillus acidophilus KNU-02-mediated bioconverted product (BLC) of Lotus corniculatus seed to reduce axillary malodor and its effect on the associated axillary microbiota. A chemical profile analysis revealed that benzoic acid was the most abundant chemical compound in BLC, which increased following bioconversion. Moreover, BLC treatment was found to reduce the intensity of axillary malodor. We tested the axillary microbiome of 18 study participants, divided equally into BLC and placebo groups, and revealed through 16S rRNA gene sequencing that Staphylococcus, Corynebacterium, and Anaerococcus were the dominant taxa, and some of these taxa were significantly associated with axillary malodor. After one week of BLC treatment, the abundance of Corynebacterium and Anaerococcus, which are associated with well-known odor-related genes that produce volatile fatty acids, had significantly reduced. Likewise, the identified odor-related genes decreased after the application of BLC. BLC treatment enhanced the richness and network density of the axillary microbial community. The placebo group, on the other hand, showed no difference in the microbial richness, odor associated taxa, and predicted functional genes after a week. The results demonstrated that BLC has the potential to reduce the axillary malodor and the associated odor-causing bacteria, which makes BLC a viable deodorant material in cosmetic products.

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