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Visualizing quantitative microscopy data: History and challenges
Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4819578/ https://www.ncbi.nlm.nih.gov/pubmed/26906253 http://dx.doi.org/10.3109/10409238.2016.1146222 |
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author | Sailem, Heba Z. Cooper, Sam Bakal, Chris |
author_facet | Sailem, Heba Z. Cooper, Sam Bakal, Chris |
author_sort | Sailem, Heba Z. |
collection | PubMed |
description | Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods. |
format | Online Article Text |
id | pubmed-4819578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-48195782016-04-22 Visualizing quantitative microscopy data: History and challenges Sailem, Heba Z. Cooper, Sam Bakal, Chris Crit Rev Biochem Mol Biol Review Article Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods. Taylor & Francis 2016-03-03 2016-02-24 /pmc/articles/PMC4819578/ /pubmed/26906253 http://dx.doi.org/10.3109/10409238.2016.1146222 Text en © 2016 The Author(s). Published by Taylor & Francis 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 work is properly cited. |
spellingShingle | Review Article Sailem, Heba Z. Cooper, Sam Bakal, Chris Visualizing quantitative microscopy data: History and challenges |
title | Visualizing quantitative microscopy data: History and challenges |
title_full | Visualizing quantitative microscopy data: History and challenges |
title_fullStr | Visualizing quantitative microscopy data: History and challenges |
title_full_unstemmed | Visualizing quantitative microscopy data: History and challenges |
title_short | Visualizing quantitative microscopy data: History and challenges |
title_sort | visualizing quantitative microscopy data: history and challenges |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4819578/ https://www.ncbi.nlm.nih.gov/pubmed/26906253 http://dx.doi.org/10.3109/10409238.2016.1146222 |
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