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Scientific Publications

Lunes, 11 Diciembre 2017 16:22

A systematic comparison of statistical methods to detect interactions in exposome-health associations.

There is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept. Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. This paper describes a simulation study in an exposome context, comparing the performance of several statistical methods that have been proposed to detect statistical interactions.

Simulations were based on an exposome including 237 exposures with a realistic correlation structure. The statistical regression-based methods used include two-step Environment-Wide Association Study (EWAS2); the Deletion/Substitution/Addition (DSA) algorithm; the Least Absolute Shrinkage and Selection Operator (LASSO); Group-Lasso INTERaction-NET (GLINTERNET); a three-step method based on regression trees and finally Boosted Regression Trees (BRT). GLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.
Download the Environ Health publication here.

MultiDataSet: an R package for encapsulating multiple data sets with application to omic data integration

Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor’s methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples.

MultiDataSet is a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. It deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. This paper illustrates the use of MultiDataSet in: performing integration analysis with third party packages; creating new methods and functions for omic data integration; encapsulating new unimplemented data from any biological experiment.
Download the publication here.

Assessment of metabolic phenotypic variability in children's urine using 1H NMR spectroscopy

This study focuses on 1 H NMR spectroscopic analyses of urine, which in comparison to blood is a non-invasive biofluid to access, making it a more attractive choice for large-scale biological sampling in children. The influence of the sample collection time-point of the day on the metabolic phenotype was examined, followed by assessment of the analyte detectability and quantification, as well as the likely sources of short term variability within and between children. We confirm the high analytical reproducibility and robustness of NMR-based urinary metabolic phenotyping and illustrate the benefits of pooling spot urines when seeking the stable component of the metabolome.
Download the publication from the Nature Scientific Reports website. 

The Pregnancy Exposome 

This review looks at initial attempts to put the exposome concept into practice, challenges and the importance of the pregnancy exposome. Several studies are reviewed that have so far addressed the relationship between the external and internal domains of the pregnancy exposome and child health and development.
Download the publication from the Current Environmental Health Reports website.

The Human Early-Life Exposome (HELIX): Project Rationale and Design.

Published in Environmental Health Perspectives, the paper describes the general design of HELIX and its main challenges, illustrating how the exposome concept may be implemented in a feasible epidemiological study design. 
Download the publication from the EHP website.

Last modified on Jueves, 14 Diciembre 2017 13:23

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