AUTOMATED METHODS TO IDENTIFY PATTERNS OF FEATURES BETWEEN CELL TYPES

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 关键词： 海量数据分析、可视化和分布协作 摘要： 培养在96孔板中的细胞使用多种方式进行成像 识别超过108个单独的生理细胞测量或 特征，包括核密度，细胞周长， 翻译后修饰，计算 S期细胞百分比与有丝分裂[Gasparri，et al.2004年。J Biol Screen 9：232-243]，细胞质-核易位， 细胞毒性和受体内化的特征。这个 该项目的目标是开发方法来识别 细胞类型之间的特征(单个生理细胞测量)。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Keywords: Volume data analysis, visualization and distribution collaboration Abstract: Cells, grown in a 96 well plate, are imaged using multiple modalities to identify over 108 individual physiological cellular measurements or features, including the nuclear density, cell perimeter, post-translational modifications, specific assays to calculate the percentage of cells in S-phase and mitosis [Gasparri, et al. 2004. J Biomol Screen 9:232-243], cytoplasm-nuclear translocation, characterization of cellular toxicity, and receptor internalization. The goal of this project is to develop approaches to identify patterns of features (individual physiological cellular measurements) between cell types.