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bigPint: A Bioconductor visualization package that makes big data pint-sized
Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconduct...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347224/ https://www.ncbi.nlm.nih.gov/pubmed/32542031 http://dx.doi.org/10.1371/journal.pcbi.1007912 |
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author | Rutter, Lindsay Cook, Dianne |
author_facet | Rutter, Lindsay Cook, Dianne |
author_sort | Rutter, Lindsay |
collection | PubMed |
description | Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks. |
format | Online Article Text |
id | pubmed-7347224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73472242020-07-20 bigPint: A Bioconductor visualization package that makes big data pint-sized Rutter, Lindsay Cook, Dianne PLoS Comput Biol Research Article Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks. Public Library of Science 2020-06-15 /pmc/articles/PMC7347224/ /pubmed/32542031 http://dx.doi.org/10.1371/journal.pcbi.1007912 Text en © 2020 Rutter, Cook 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 Rutter, Lindsay Cook, Dianne bigPint: A Bioconductor visualization package that makes big data pint-sized |
title | bigPint: A Bioconductor visualization package that makes big data pint-sized |
title_full | bigPint: A Bioconductor visualization package that makes big data pint-sized |
title_fullStr | bigPint: A Bioconductor visualization package that makes big data pint-sized |
title_full_unstemmed | bigPint: A Bioconductor visualization package that makes big data pint-sized |
title_short | bigPint: A Bioconductor visualization package that makes big data pint-sized |
title_sort | bigpint: a bioconductor visualization package that makes big data pint-sized |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347224/ https://www.ncbi.nlm.nih.gov/pubmed/32542031 http://dx.doi.org/10.1371/journal.pcbi.1007912 |
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