Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.
Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.
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DOI：
10.1186/1471-2121-8-s1-s3
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发表时间：
2007-07-10
期刊：
影响因子：
--
通讯作者：
Knowles, David W.
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文献类型：
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作者：
Long, Fuhui;Peng, Hanchuan;Sudar, Damir;Lelievre, Sophie A.;Knowles, David W.
染色质相关蛋白的分布在指导核功能中起着关键作用。此前，我们开发了一种基于图像的方法来量化蛋白质的核分布，并表明这些分布取决于人类乳腺上皮细胞的表型。在这里，我们描述了一种方法，该方法创建给定细胞表型的层次树，并根据核蛋白分布的聚类分析计算它们之间的统计显着性。之前获得了培养长达 12 天的非肿瘤性 S1 和恶性 T4-2 人乳腺上皮细胞的核有丝分裂装置蛋白的核分布。细胞表型定义为S1或T4-2以及培养天数。概率集成方法用于根据应用于核分布数据的多种传统聚类分析技术的结果来定义一组共识聚类。构建簇直方图是为了显示任何一种表型的细胞如何分布在共有簇中。对各种表型进行分组使我们能够构建表型树并计算每组之间的统计差异。结果显示，能够区分非肿瘤性S1细胞和恶性T4-2细胞，准确率达94.19%；能够以 92.86% 的准确率区分增殖的 S1 细胞和分化的 S1 细胞；并且随着肿瘤大小的增加，T4-2细胞的各种表型之间没有显着差异。这项工作提出了一种聚类分析方法，可以根据特定蛋白质的核分布以高精度识别重要的细胞表型。
The distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions. Nuclear distributions of nuclear mitotic apparatus protein were previously obtained for non-neoplastic S1 and malignant T4-2 human mammary epithelial cells cultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 and the number of days in cultured. A probabilistic ensemble approach was used to define a set of consensus clusters from the results of multiple traditional cluster analysis techniques applied to the nuclear distribution data. Cluster histograms were constructed to show how cells in any one phenotype were distributed across the consensus clusters. Grouping various phenotypes allowed us to build phenotype trees and calculate the statistical difference between each group. The results showed that non-neoplastic S1 cells could be distinguished from malignant T4-2 cells with 94.19% accuracy; that proliferating S1 cells could be distinguished from differentiated S1 cells with 92.86% accuracy; and showed no significant difference between the various phenotypes of T4-2 cells corresponding to increasing tumor sizes. This work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.