Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis
Abstract
Background: 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. Results: 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 bemore »
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division; Howard Hughes Medical Inst., Ashburn, VA (United States). Janelia Farm Research Campus
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division West; Howard Hughes Medical Inst., Ashburn, VA (United States). Janelia Farm Research Campus
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division
- Purdue Univ., West Lafayette, IN (United States). Dept. of Basic Medical Science
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1626366
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Cell Biology
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: Suppl 1; Journal ID: ISSN 1471-2121
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Cell Biology
Citation Formats
Long, Fuhui, Peng, Hanchuan, Sudar, Damir, Lelièvre, Sophie A., and Knowles, David W. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. United States: N. p., 2007.
Web. doi:10.1186/1471-2121-8-s1-s3.
Long, Fuhui, Peng, Hanchuan, Sudar, Damir, Lelièvre, Sophie A., & Knowles, David W. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. United States. https://doi.org/10.1186/1471-2121-8-s1-s3
Long, Fuhui, Peng, Hanchuan, Sudar, Damir, Lelièvre, Sophie A., and Knowles, David W. Tue .
"Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis". United States. https://doi.org/10.1186/1471-2121-8-s1-s3. https://www.osti.gov/servlets/purl/1626366.
@article{osti_1626366,
title = {Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis},
author = {Long, Fuhui and Peng, Hanchuan and Sudar, Damir and Lelièvre, Sophie A. and Knowles, David W.},
abstractNote = {Background: 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. Results: 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. Conclusion: This work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.},
doi = {10.1186/1471-2121-8-s1-s3},
journal = {BMC Cell Biology},
number = Suppl 1,
volume = 8,
place = {United States},
year = {Tue Jul 10 00:00:00 EDT 2007},
month = {Tue Jul 10 00:00:00 EDT 2007}
}
Works referenced in this record:
Nuclear structure in cancer cells
journal, September 2004
- Zink, Daniele; Fischer, Andrew H.; Nickerson, Jeffrey A.
- Nature Reviews Cancer, Vol. 4, Issue 9
Tissue phenotype depends on reciprocal interactions between the extracellular matrix and the structural organization of the nucleus
journal, December 1998
- Lelievre, S. A.; Weaver, V. M.; Nickerson, J. A.
- Proceedings of the National Academy of Sciences, Vol. 95, Issue 25
Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype
journal, March 2006
- Knowles, David W.; Sudar, Damir; Bator-Kelly, Carol
- Proceedings of the National Academy of Sciences, Vol. 103, Issue 12
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
journal, January 1973
- Dunn, J. C.
- Journal of Cybernetics, Vol. 3, Issue 3
Bayesian approaches to Gaussian mixture modeling
journal, January 1998
- Roberts, S. J.; Husmeier, D.; Rezek, I.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, Issue 11
Data clustering: a review
journal, September 1999
- Jain, A. K.; Murty, M. N.; Flynn, P. J.
- ACM Computing Surveys, Vol. 31, Issue 3, p. 264-323
An optimal graph theoretic approach to data clustering: theory and its application to image segmentation
journal, January 1993
- Wu, Z.; Leahy, R.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, Issue 11
Combining multiple weak clusterings
conference,
- Topchy, A.; Jain, A. K.; Punch, W.
- Third IEEE International Conference on Data Mining
Bagging for path-based clustering
journal, November 2003
- Fischer, B.; Buhmann, J. M.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, Issue 11
Bagging to improve the accuracy of a clustering procedure
journal, June 2003
- Dudoit, S.; Fridlyand, J.
- Bioinformatics, Vol. 19, Issue 9
Efficient Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables
journal, January 1997
- Maxwell Chickering, David; Heckerman, David
- Machine Learning, Vol. 29, Issue 2/3, p. 181-212
Unravelling heterochromatin: competition between positive and negative factors regulates accessibility
journal, May 2002
- Dillon, Niall; Festenstein, Richard
- Trends in Genetics, Vol. 18, Issue 5
Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype
journal, March 2006
- Knowles, David W.; Sudar, Damir; Bator-Kelly, Carol
- Proceedings of the National Academy of Sciences, Vol. 103, Issue 12
Tissue phenotype depends on reciprocal interactions between the extracellular matrix and the structural organization of the nucleus
journal, December 1998
- Lelievre, S. A.; Weaver, V. M.; Nickerson, J. A.
- Proceedings of the National Academy of Sciences, Vol. 95, Issue 25
NuMA Influences Higher Order Chromatin Organization in Human Mammary Epithelium
journal, February 2007
- Abad, Patricia C.; Lewis, Jason; Mian, I. Saira
- Molecular Biology of the Cell, Vol. 18, Issue 2
Bagging to improve the accuracy of a clustering procedure
journal, June 2003
- Dudoit, S.; Fridlyand, J.
- Bioinformatics, Vol. 19, Issue 9
An optimal graph theoretic approach to data clustering: theory and its application to image segmentation
journal, January 1993
- Wu, Z.; Leahy, R.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, Issue 11
Bayesian approaches to Gaussian mixture modeling
journal, January 1998
- Roberts, S. J.; Husmeier, D.; Rezek, I.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, Issue 11
A Bayesian Morphometry Algorithm
journal, June 2004
- Herskovits, E. H.; Peng, H.; Davatzikos, C.
- IEEE Transactions on Medical Imaging, Vol. 23, Issue 6
A Mixture Model for Clustering Ensembles
conference, April 2004
- Topchy, Alexander; Jain, Anil K.; Punch, William
- Proceedings of the 2004 SIAM International Conference on Data Mining
Data clustering: a review
journal, September 1999
- Jain, A. K.; Murty, M. N.; Flynn, P. J.
- ACM Computing Surveys, Vol. 31, Issue 3, p. 264-323
Cell Nucleus in Context
journal, January 2000
- Lelievre, Sophie; Bisseil, Mina J.; Pujuguet, Philippe
- Critical Reviews™ in Eukaryotic Gene Expression, Vol. 10, Issue 1
Works referencing / citing this record:
Bioimage informatics: a new area of engineering biology
journal, July 2008
- Peng, Hanchuan
- Bioinformatics, Vol. 24, Issue 17
Quantification and its Applications in Fluorescent Microscopy Imaging
journal, August 2009
- Hamilton, Nicholas
- Traffic, Vol. 10, Issue 8