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

Hernández-Ferrer, Carles; Ruiz‐Arenas, Carlos; Beltran-Gomila, Alba; González, Juan R.

doi:10.1186/s12859-016-1455-1

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…All the analyses were done in R environment. The R packages MultiDataSet [ 59 ], rexposome, and omicRexposome [ 41 ] were used to manage and analyze the omics and exposure data. ggplot2 [ 60 ], qqman [ 61 ], calibrate [ 62 ], sjPlot [ 63 ], OmicCircos [ 64 ], and coMET [ 65 ] R packages were used to visualize the results.…”

Section: Methodsmentioning

confidence: 99%

In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children

Vives-Usano

Hernández-Ferrer

Maitre

et al. 2020

BMC Med

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Background: The adverse health effects of early life exposure to tobacco smoking have been widely reported. In spite of this, the underlying molecular mechanisms of in utero and postnatal exposure to tobacco smoke are only partially understood. Here, we aimed to identify multi-layer molecular signatures associated with exposure to tobacco smoke in these two exposure windows. Methods: We investigated the associations of maternal smoking during pregnancy and childhood secondhand smoke (SHS) exposure with molecular features measured in 1203 European children (mean age 8.1 years) from the Human Early Life Exposome (HELIX) project. Molecular features, covering 4 layers, included blood DNA methylation and gene and miRNA transcription, plasma proteins, and sera and urinary metabolites. Results: Maternal smoking during pregnancy was associated with DNA methylation changes at 18 loci in child blood. DNA methylation at 5 of these loci was related to expression of the nearby genes. However, the expression of these genes themselves was only weakly associated with maternal smoking. Conversely, childhood SHS was not associated with blood DNA methylation or transcription patterns, but with reduced levels of several serum metabolites and with increased plasma PAI1 (plasminogen activator inhibitor-1), a protein that inhibits fibrinolysis. Some of the in utero and childhood smoking-related molecular marks showed dose-response trends, with stronger effects with higher dose or longer duration of the exposure.

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Section: Methodsmentioning

confidence: 99%

In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children

Vives-Usano

Hernández-Ferrer

Maitre

et al. 2020

BMC Med

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“…Some investigators have used the WGCNA approach (Langfelder & Horvath 2008) adopted from transcriptomics/microarray analyses for integrated omics workflows. While this has been useful, it does not provide a means to address unique data structures of different omics datasets in biomedical research (Smith et al 2007) To this end, recently, an R-package, MultiDataSet was proposed for encapsulating multiple data sets with application to -omics data integration, keeping in mind the different data structure (list of matrices) generated from individual omics datasets (Hernandez-Ferrer et al 2017).…”

Section: Integration Issues -Data Scaling False Positives and Unknownsmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

374

240

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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

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“…The need for a unified data model for multi-omics experiments has been recognized in other projects, such as MultiDataSet (4) and CNAMet (5). Our developments are motivated by an interest in bridging effective single-assay Application Program Interface (API) elements, including endomorphic feature and sample subset operations, to multi-omic contexts of arbitrary complexity and volume (Supplemental Table 1).…”

Section: Introductionmentioning

confidence: 99%

Software for the Integration of Multiomics Experiments in Bioconductor

Ramos

Schiffer

et al. 2017

Cancer Research

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Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas (TCGA) as ready-to-analyze MultiAssayExperiment objects, and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable and reproducible statistical analysis of multi-omics data and enhances data science applications of multiple omics datasets.

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

Cited by 22 publications

References 22 publications

In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children

In utero and childhood exposure to tobacco smoke and multi-layer molecular signatures in children

Integrated omics: tools, advances and future approaches

Software for the Integration of Multiomics Experiments in Bioconductor

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