a probabilistic model for cell population phenotyping using hcs data细胞群表型出现的概率模型使用高碳钢数据.pdfVIP

A Probabilistic Model for Cell Population Phenotyping Using HCS Data 1,2,3 2,4 2,3 6 6 Edouard Pauwels *, Didier Surdez , Gautier Stoll , Aurianne Lescure , Elaine Del Nery , 2,4,5 ´ 1,2,3 Olivier Delattre , Veronique Stoven ´ 1 Mines ParisTech, Centre for Computational Biology, Fontainebleau Cedex, France, 2 Institut Curie, Paris, France, 3 Institut national de la sante et de la recherche ´ ´ ´ ´ ´ ´ ´ medicale, Paris, France, 4 Institut national de la sante et de la recherche medicale, Unite de Genetique et Biologie des Cancers, Paris, France, 5 Institut Curie, Unite de ´ ´ genetique somatique, Paris, France, 6 The Biophenics Automated Imaging Laboratory, Department of Translational Research, Institut Curie, Paris, France Abstract High Content Screening (HCS) platforms allow screening living cells under a wide range of experimental conditions and give access to a whole panel of cellular responses to a specific treatment. The outcome is a series of cell population images. Within these images, the heterogeneity of cellular response to the same treatment leads to a whole range of observed values for the recorded cellular features. Consequently, it is difficult to compare and interpret experiments. Moreover, the definition of phenotypic classes at a cell population level remains an open question, although this would ease experiments