Dataset Information

Composite score analysis for unsupervised comparison and network visualization of metabolomics data.

ABSTRACT: Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three principal components limits the comprehensiveness of the model and can mask discrimination between samples. We have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to calculate a correlation matrix that enables quantitative comparisons of sample similarity between samples within one dataset based upon measured metabolome profiles. Composite score values were tabulated using profiles of complex extracts of dietary supplements from the plant Hydrastis canadensis (goldenseal) as a case study. Several outliers were unambiguously identified, and a PCA composite score network was developed to provide a graphical representation of the composite score matrix. Comparison with visualization using PCA score plots or dendrograms from hierarchical clustering analysis (HCA) demonstrates the utility of the composite score to as a tool for metabolomics studies that seek to quantify similarity among samples. An R-script for the calculation of composite score has been made available.

SUBMITTER: Kellogg JJ

PROVIDER: S-EPMC6948848 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Composite score analysis for unsupervised comparison and network visualization of metabolomics data.

Kellogg Joshua J JJ Kvalheim Olav M OM Cech Nadja B NB

Analytica chimica acta 20191016

Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three p ...[more]

PMID: 31864629

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Project description:Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

Project description:IntroductionThe Alzheimer's disease (AD) composite score (ADCOMS) has been shown to be a more sensitive measure of cognitive change in early AD (i.e., mild cognitive impairment [MCI] and mild AD) than commonly used measures. This study derived ADCOMS value ranges associated with different disease severity stages across the predementia and AD continuum.MethodsData from patients enrolled in the Alzheimer's Disease Neuroimaging Initiative were assessed at baseline and month 24. Data were randomly split into derivation and validation samples. Receiver-operating characteristic (ROC) curves of ADCOMS values were generated in the derivation sample to assess the sensitivity and specificity of ADCOMS cutoff values compared with existing disease severity cutoff scores using the Clinical Dementia Rating (CDR) global, CDR Sum of Boxes, Alzheimer's Disease Assessment Scale-Cognitive Subscale, and Mini-Mental State Examination. Optimal ADCOMS cutoff values for each disease stage were compared between the derivation and the validation samples using a χ2 test. The diagnostic accuracy of the derived ADCOMS cutoff values was then assessed. The analyses were repeated for the subset with positive amyloid β confirmation (Aβ +).ResultsThe following ADCOMS value ranges for the total population and Aβ + population were identified: < 0.29 indicative of normal cognition, 0.29 to < 0.45 indicative of MCI, 0.45-0.77 indicative of mild AD, and > 0.77 indicative of at least moderate AD. The reliability of these ADCOMS value ranges was supported by diagnostic accuracy tests and tests indicating no significant difference in the ROC curves between the derivation and validation samples.ConclusionADCOMS values ranges can be used to assess the severity of cognitive decline. The derived severity threshold score ranges for ADCOMS will enable its use as an endpoint in clinical trials assessing disease progression and clinical outcomes of disease-modifying therapies in persons with MCI or early AD, including patients with Aβ + confirmation.

Dataset Information

Composite score analysis for unsupervised comparison and network visualization of metabolomics data.

Publications

Composite score analysis for unsupervised comparison and network visualization of metabolomics data.

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