Reducing the multidimensionality of high-content screening into versatile powerful descriptors

High-content image analysis captures many cellular parameters, but current methods of interpretation of acquired multiple dimensions assume a normal distribution, which is rarely seen in biological data sets. We describe a novel statistically based approach that collapses a set of cellular measureme...

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Bibliographic Details
Main Authors: Julie Gorenstein, Ben Zack, Joseph R. Marszalek, Ansu Bagchi, Sai Subramaniam, Pamela Carroll, Cem Elbi
Format: Article
Language:English
Published: Taylor & Francis Group 2010-09-01
Series:BioTechniques
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Online Access:https://www.future-science.com/doi/10.2144/000113492
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