title: Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry.
abstract: Modern microscopic imaging devices are able to extract more information regarding the subcellular organization of different molecules and proteins than can be obtained by visual inspection. Predetermined numerical features (descriptors) often used to quantify cells extracted from these images have long been shown useful for discriminating cell populations (e.g., normal vs. diseased). Direct visual or biological interpretation of results obtained, however, is often not a trivial task. We describe an approach for detecting and visualizing phenotypic differences between classes of cells based on the theory of optimal mass transport. The method is completely automated, does not require the use of predefined numerical features, and at the same time allows for easily interpretable visualizations of the most significant differences. Using this method, we demonstrate that the distribution pattern of peripheral chromatin in the nuclei of cells extracted from liver and thyroid specimens is associated with malignancy. We also show the method can correctly recover biologically interpretable and statistically significant differences in translocation imaging assays in a completely automated fashion.
authors.affiliation: Center for Bioimage Informatics, Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213.
authors.forename: Saurav
authors.initials: S
authors.lastname: Basu
authors.forename: Soheil
authors.initials: S
authors.lastname: Kolouri
authors.forename: Gustavo K
authors.initials: GK
authors.lastname: Rohde
mesh_terms: D000465:Algorithms; D002477:Cells / Q000166:cytology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods* / Q000639:trends; D057054:Molecular Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D010641:Phenotype; D025341:Principal Component Analysis / Q000379:methods
keywords: cell morphometry; high content screening; optimal transport