Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for hypercholesterolemia

2021 ◽  
Vol 553 ◽  
pp. 1-8
Author(s):  
Ya-nan OuYang ◽  
Lu-xin Zhou ◽  
Yue-xin Jin ◽  
Guo-feng Hou ◽  
Peng-fei Yang ◽  
...  
Keyword(s):  
Metabolomics Data ◽  
Correlation Networks

scholarly journals SCNIC: Sparse Correlation Network Investigation for Compositional Data

2020 ◽  
Author(s):  
Michael Shaffer ◽  
Kumar Thurimella ◽  
Catherine A. Lozupone
Keyword(s):  
Statistical Power ◽  
Compositional Data ◽  
Small Sample ◽  
Environmental Drivers ◽  
Data Types ◽  
Metabolomics Data ◽  
Microbiome Composition ◽  
Cooperative Relationships ◽  
Correlation Networks ◽  
Highly Correlated

ABSTRACTBackgroundMicrobiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations. Additionally, modules provide biological insight as groups of microbes can have relationships among themselves.ResultsTo address these challenges we developed SCNIC: Sparse Cooccurrence Network Investigation for Compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. We applied SCNIC to a published dataset comparing microbiome composition in men who have sex with men (MSM) who were at a high risk of contracting HIV to non-MSM. By applying SCNIC we achieved increased statistical power and identified microbes that not only differed with MSM-status, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them.ConclusionsSCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. Using SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power.

scholarly journals Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for young hypertensive men

2015 ◽  
Vol 854 ◽  
pp. 95-105 ◽  
Author(s):  
Le Wang ◽  
Entai Hou ◽  
Lijun Wang ◽  
Yanjun Wang ◽  
Lingjian Yang ◽  
...  
Keyword(s):  
Metabolomics Data ◽  
Correlation Networks

scholarly journals Repository scale classification and decomposition of tandem mass spectral data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mihir Mongia ◽  
Hosein Mohimani
Keyword(s):  
High Accuracy ◽  
Mass Spectral Data ◽  
Metabolomics Data ◽  
Mass Spectral ◽  
Molecular Features ◽  
Oriented Samples ◽  
Ingredient Composition ◽  
Single Phenotype ◽  
Tandem Mass Spectral Data ◽  
Scale Classification

AbstractVarious studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

scholarly journals Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Biting Wang ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Molecular Docking ◽  
Liver Fibrosis ◽  
Molecular Mechanisms ◽  
Docking Simulation ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Bipartite Network ◽  
Metabolomics Data ◽  
Metabolic Products ◽  
Compound Target

Abstract Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

scholarly journals COVID-19 lockdown has altered the dynamics between affective symptoms and social isolation among older adults: results from a longitudinal network analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junhong Yu ◽  
Rathi Mahendran
Keyword(s):  
Older Adults ◽  
Social Isolation ◽  
Anxiety Symptoms ◽  
Community Dwelling ◽  
Depression Symptoms ◽  
Network Analyses ◽  
Correlation Networks ◽  
Affective Symptoms ◽  
Tightly Coupled ◽  
Community Dwelling Older Adults

AbstractThe COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

scholarly journals Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  
Keyword(s):  
Regression Model ◽  
Inborn Errors Of Metabolism ◽  
Untargeted Metabolomics ◽  
Detection Accuracy ◽  
Inborn Errors ◽  
Mixed Effect ◽  
Metabolomics Data ◽  
Normalization Methods ◽  
Metabolite Concentrations ◽  
Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

Sign in / Sign up

Export Citation Format

Share Document