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An R package for divergence analysis of omics data

Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code b...

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Detalles Bibliográficos
Autores principales: Dinalankara, Wikum, Ke, Qian, Geman, Donald, Marchionni, Luigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021195/
https://www.ncbi.nlm.nih.gov/pubmed/33819273
http://dx.doi.org/10.1371/journal.pone.0249002
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author Dinalankara, Wikum
Ke, Qian
Geman, Donald
Marchionni, Luigi
author_facet Dinalankara, Wikum
Ke, Qian
Geman, Donald
Marchionni, Luigi
author_sort Dinalankara, Wikum
collection PubMed
description Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas.
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spelling pubmed-80211952021-04-14 An R package for divergence analysis of omics data Dinalankara, Wikum Ke, Qian Geman, Donald Marchionni, Luigi PLoS One Research Article Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with data from the Cancer Genome Atlas. Public Library of Science 2021-04-05 /pmc/articles/PMC8021195/ /pubmed/33819273 http://dx.doi.org/10.1371/journal.pone.0249002 Text en © 2021 Dinalankara et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Dinalankara, Wikum
Ke, Qian
Geman, Donald
Marchionni, Luigi
An R package for divergence analysis of omics data
title An R package for divergence analysis of omics data
title_full An R package for divergence analysis of omics data
title_fullStr An R package for divergence analysis of omics data
title_full_unstemmed An R package for divergence analysis of omics data
title_short An R package for divergence analysis of omics data
title_sort r package for divergence analysis of omics data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021195/
https://www.ncbi.nlm.nih.gov/pubmed/33819273
http://dx.doi.org/10.1371/journal.pone.0249002
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