pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge

Wachter, Astrid; Beißbarth, Tim

doi:10.1093/bioinformatics/btv323

Cited by 32 publications

(29 citation statements)

References 20 publications

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“…This need is evident in the recent manuscripts published addressing the need for data integration, with various sophisticated bioinformatics strategies proposed to integrate the variety of epigenomic and other “omics” data sets produced by scientists around the world (Génin and Devoto 2015; Gomez-Cabrero et al 2014; Pineda et al 2015; Saha et al 2014; Wachter and Beißbarth 2015; Zierer et al 2015). In addition, large consortia efforts such as the NIH Roadmap Epigenomics Mapping Consortium, curate data on DNA methylation, mRNA expression, and changes in histones and in chromatin accessibility, annotating these data across a sweeping array of human cell types and creating genome-wide annotation maps.…”

Section: The Future Of Environmental Epigenetics In Children’s Healthmentioning

confidence: 99%

Small-Magnitude Effect Sizes in Epigenetic End Points are Important in Children’s Environmental Health Studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group

Breton

Marsit

Faustman

et al. 2017

Environ Health Perspect

234

159

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Background:Characterization of the epigenome is a primary interest for children’s environmental health researchers studying the environmental influences on human populations, particularly those studying the role of pregnancy and early-life exposures on later-in-life health outcomes.Objectives:Our objective was to consider the state of the science in environmental epigenetics research and to focus on DNA methylation and the collective observations of many studies being conducted within the Children’s Environmental Health and Disease Prevention Research Centers, as they relate to the Developmental Origins of Health and Disease (DOHaD) hypothesis.Methods:We address the current laboratory and statistical tools available for epigenetic analyses, discuss methods for validation and interpretation of findings, particularly when magnitudes of effect are small, question the functional relevance of findings, and discuss the future for environmental epigenetics research.Discussion:A common finding in environmental epigenetic studies is the small-magnitude epigenetic effect sizes that result from such exposures. Although it is reasonable and necessary that we question the relevance of such small effects, we present examples in which small effects persist and have been replicated across populations and across time. We encourage a critical discourse on the interpretation of such small changes and further research on their functional relevance for children’s health.Conclusion:The dynamic nature of the epigenome will require an emphasis on future longitudinal studies in which the epigenome is profiled over time, over changing environmental exposures, and over generations to better understand the multiple ways in which the epigenome may respond to environmental stimuli.Citation:Breton CV, Marsit CJ, Faustman E, Nadeau K, Goodrich JM, Dolinoy DC, Herbstman J, Holland N, LaSalle JM, Schmidt R, Yousefi P, Perera F, Joubert BR, Wiemels J, Taylor M, Yang IV, Chen R, Hew KM, Freeland DM, Miller R, Murphy SK. 2017. Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: the Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group. Environ Health Perspect 125:–526; http://dx.doi.org/10.1289/EHP595

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Section: The Future Of Environmental Epigenetics In Children’s Healthmentioning

confidence: 99%

Small-Magnitude Effect Sizes in Epigenetic End Points are Important in Children’s Environmental Health Studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group

Breton

Marsit

Faustman

et al. 2017

Environ Health Perspect

234

159

View full text Add to dashboard Cite

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“…Similar effects can be assumed for other types of analyses with high-throughput expression data, such as gene set analysis [46–48], classification problems [18–20], and in consequence also for data integration methods of multi-omics data [49, 50]. The concrete handling of outliers detected by our approach depends of course on the specific methods for subsequent analysis.…”

Section: Discussionmentioning

confidence: 72%

Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots

Kruppa

Jung

2017

BMC Bioinformatics

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BackgroundAnalyses of molecular high-throughput data often lack in robustness, i.e. results are very sensitive to the addition or removal of a single observation. Therefore, the identification of extreme observations is an important step of quality control before doing further data analysis. Standard outlier detection methods for univariate data are however not applicable, since the considered data are high-dimensional, i.e. multiple hundreds or thousands of features are observed in small samples. Usually, outliers in high-dimensional data are solely detected by visual inspection of a graphical representation of the data by the analyst. Typical graphical representation for high-dimensional data are hierarchical cluster tree or principal component plots. Pure visual approaches depend, however, on the individual judgement of the analyst and are hard to automate. Existing methods for automated outlier detection are only dedicated to data of a single experimental groups.ResultsIn this work we propose to use bagplots, the 2-dimensional extension of the boxplot, to automatically identify outliers in the subspace of the first two principal components of the data. Furthermore, we present for the first time the gemplot, the 3-dimensional extension of boxplot and bagplot, which can be used in the subspace of the first three principal components. Bagplot and gemplot surround the regular observations with convex hulls and observations outside these hulls are regarded as outliers. The convex hulls are determined separately for the observations of each experimental group while the observations of all groups can be displayed in the same subspace of principal components. We demonstrate the usefulness of this approach on multiple sets of artificial data as well as one set of gene expression data from a next-generation sequencing experiment, and compare the new method to other common approaches. Furthermore, we provide an implementation of the gemplot in the package ‘gemPlot’ for the R programming environment.ConclusionsBagplots and gemplots in subspaces of principal components are useful for automated and objective outlier identification in high-dimensional data from molecular high-throughput experiments. A clear advantage over other methods is that multiple experimental groups can be displayed in the same figure although outlier detection is performed for each individual group.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1645-5) contains supplementary material, which is available to authorized users.

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“…A more selective list of free packages, compared in the bioinformatics application context, is compiled in Paluszewski and Hamelryck (2010), with a special emphasis on the dynamic BNs (DBNs). pwOmics (Watcher and Beisbarth, 2015) is the most recent implementation of DBN modeling in omics data context. Another recent BN reconstruction algorithm (Jiang et al, 2010a, 2010b; Jiang and Neapolitan, 2012) is of special interest as the authors attempted to increase the scalability of BN modeling to make it directly usable with the large-scale genetic epidemiology datasets.…”

Section: Existing Algorithms and Software Packagesmentioning

confidence: 99%

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

Gogoshin

Boerwinkle

Rodin

2017

Journal of Computational Biology

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Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology—type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types—single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.

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pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge

Abstract: astrid.wachter@med.uni-goettingen.de.

Cited by 32 publications

References 20 publications

Small-Magnitude Effect Sizes in Epigenetic End Points are Important in Children’s Environmental Health Studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group

Small-Magnitude Effect Sizes in Epigenetic End Points are Important in Children’s Environmental Health Studies: The Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group

Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data

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