Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families

Draisma, Harmen H.M.; Reijmers, Th.H.; Meulman, Jacqueline J.; Greef, J. van der; Hankemeier, Thomas; Boomsma, Dorret I.

doi:10.1038/ejhg.2012.110

Cited by 36 publications

(26 citation statements)

References 29 publications

(37 reference statements)

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“…It is used to make data easy to explore and visualize. Another unsupervised method for this purpose is the Hierarchical Cluster Analysis (HCA) (8,44). The most used supervised classification methods are k-nearest-Neighbors (kNN) (1,38), Soft Indipendent Modeling of Class Analogy (SIMCA) (12) and Partial Last Squares Discriminant Analysis (PLS-DA) (3,14).…”

Section: Chemometrics (Statistical Analysis)mentioning

confidence: 99%

The omics era: what can nuclear magnetic resonance tell us on metabolomics?

2018

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A brief overview of the potentiality and use of the metabolic fingerprint of a system or biological process is here proposed. The information on the type, quantity and variation of the pool of metabolites and its relationship with a given biological process is commonly referred to as metabolomics. One powerful analytical approach to the detection and quantitation of metabolites is by Nuclear Magnetic Resonance Spectroscopy (NMR). Additionally, the recently introduced High Resolution Magic Angle Spinning (HR-MAS) NMR approach improved dramatically the potentiality of the method allowing direct sampling of ex vivo specimens, such as tissues and cells, without any pre-treatment or extraction steps. The NMR data can be processed towards the target or non-target analysis of the metabolites. The former passes through the identification of all the metabolites, the latter adopts a multivariate statistical approach such as Principal Components Analysis. In this article, the main methodological points of NMR analysis with multivariate statistics are briefly outlined and discussed. A final case-study on the discrimination of healthy and neoplastic tissues via HR-MAS NMR metabolomics is reported as a paradigmatic application.

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Section: Chemometrics (Statistical Analysis)mentioning

confidence: 99%

The omics era: what can nuclear magnetic resonance tell us on metabolomics?

2018

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“…This graph helps us visualize the degree to which bacteria were associated with the same or different metabolites. Another similar example of using hierarchical clustering together with heat map representation can be found in Draisma et al (2013), which used hierarchical clustering to analyze blood plasma lipid profiles of twins.…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Computational and statistical analysis of metabolomics data

et al. 2015

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Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomics, it is common to deal with large amounts of data generated by nuclear magnetic resonance (NMR) and/or mass spectrometry (MS). Moreover, based on different goals and designs of studies, it may be necessary to use a variety of data analysis methods or a combination of them in order to obtain an accurate and comprehensive result. In this review, we intend to provide an overview of computational and statistical methods that are commonly applied to analyze metabolomics data. The review is divided into five sections. The first two sections will introduce the background and the databases and resources available for metabolomics research. The third section will briefly describe the principles of the two main experimental methods that produce metabolomics data: MS and NMR, followed by the fourth section that describes the preprocessing of the data from these two approaches. In the fifth and the most important section, we will review four main types of analysis that can be performed on metabolomics data with examples in metabolomics. These are unsupervised learning methods, supervised learning methods, pathway analysis methods and analysis of time course metabolomics data. We conclude by providing a table summarizing the principles and tools that we discussed in this review.

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“…A top‐down example is our study of post‐mortem brain tissue genome‐wide expression data : we defined a gene co‐expression network based on the pair‐wise Pearson correlations between expression levels of genes, and then identified modules of co‐expressed genes via a clustering algorithm, per the weighted gene co‐expression network analysis (WGCNA) method of Zhang and Horvath . Top‐down networks are statistically inferred from the data, using one of a number of different network models, including hierarchical clustering and random forest . Principal component analysis (PCA) and independent component analysis (ICA), among others, are powerful tools for retrieving underlying biological features from complex networks.…”

Section: Introduction To Molecular Networkmentioning

confidence: 99%

Molecular network analysis enhances understanding of the biology of mental disorders

et al. 2014

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Summary We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects.

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Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families

Cited by 36 publications

References 29 publications

The omics era: what can nuclear magnetic resonance tell us on metabolomics?

The omics era: what can nuclear magnetic resonance tell us on metabolomics?

Computational and statistical analysis of metabolomics data

Molecular network analysis enhances understanding of the biology of mental disorders

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