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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8021195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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