ArticleBMC cell biology2007
Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.
Article in BMC cell biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data.Bioinformatics (Oxford, England) · 2022Article
- PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection.Briefings in bioinformatics · 2021Article
- Bioimage informatics: a new area of engineering biology.Bioinformatics (Oxford, England) · 2008Review
- Proceedings of the 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics, Santa Barbara, USA (BII06).BMC cell biology · 2007Article
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5 authors.
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Abstract
backgroundThe 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.
resultsNuclear 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.
conclusionThis work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.
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