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Heba Sailem, Miguel Sanchez-Alvarez, Julia Sero, Chris Bakal (London, UK)Cellular imaging plays an important role in biological discoveries. High content analysis of cellular images produces large multivariate data that describes complex phenotypes. The main methods that are used to visualize high content data are heat maps or coordinate-based graphs. However, these methods are limited to three-dimensional representations, or difficult to relate to cellular phenotypes, such as cell shape. Thus methods that represent high-dimensional cellular phenotype data in an intuitive way are still lacking. Here we design and develop a novel visualization method; PhenoPlot that simulates various aspects of cellular structures to represent imaging data in a concise and a quantitative way. PhenoPlot is available as a Matlab toolbox and allows plotting up to 22 variables using cell-like glyphs with combination of color based elements and proposes a novel visualization concept; Proportional Filling. Furthermore, PhenoPlot representation is independent of XY coordinates, which makes it a flexible tool to visualize imaging data. We illustrate the power of PhenoPlot in gaining insight into the morphological heterogeneity of 19 breast cancer cell lines.