Abstract
Visualization is essential for data interpretation, hypothesis formulation and communication of results. However, there is a paucity of visualization methods for image-derived data sets generated by high-content analysis in which complex cellular phenotypes are described as high-dimensional vectors of features. Here we present a visualization tool, PhenoPlot, which represents quantitative high-content imaging data as easily interpretable glyphs, and we illustrate how PhenoPlot can be used to improve the exploration and interpretation of complex breast cancer cell phenotypes.
Original language | English |
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Article number | 5825 |
Journal | Nature Communications |
Volume | 6 |
DOIs | |
Publication status | Published - 8 Jan 2015 |
Keywords
- computer vision
- data visualisation
- High content imaging
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Julia Sero
- Department of Life Sciences - Lecturer
- Centre for Therapeutic Innovation
- Institute for Mathematical Innovation (IMI)
Person: Research & Teaching