1. Comparison of Methods for Image-Based Profiling of Cellular Morphological Responses to Small-Molecule Treatment
- Author
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Emma Jenkins, Shantanu Singh, Sandeep Daya, Vebjorn Ljosa, Auguste Genovesio, Neil O. Carragher, Mark E. Roberts, Paul A. Clemons, Katherine L. Sokolnicki, Peter D. Caie, Rob ter Horst, Thouis R. Jones, Anne E. Carpenter, Bournemouth University [Poole] (BU), CCLRC Daresbury Laboratory, Institut Curie [Paris], Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de biologie de l'ENS Paris (IBENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
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Support Vector Machine ,Phenotypic screening ,Population ,Drug Evaluation, Preclinical ,Computational biology ,Biology ,computer.software_genre ,01 natural sciences ,Biochemistry ,Article ,Analytical Chemistry ,Small Molecule Libraries ,03 medical and health sciences ,Humans ,Profiling (information science) ,[INFO]Computer Science [cs] ,[INFO.INFO-BT]Computer Science [cs]/Biotechnology ,education ,Cell Shape ,ComputingMilieux_MISCELLANEOUS ,030304 developmental biology ,Genetics ,0303 health sciences ,education.field_of_study ,Scanning Probe Microscopy ,0104 chemical sciences ,Software framework ,010404 medicinal & biomolecular chemistry ,Phenotype ,Microscopy, Fluorescence ,High-content screening ,Quantitative Microscopy ,MCF-7 Cells ,Energy and redox metabolism Mitochondrial medicine [NCMLS 4] ,Molecular Medicine ,Factor Analysis, Statistical ,computer ,Biotechnology ,Test data - Abstract
Item does not contain fulltext Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules' effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound's mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.
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- 2013
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