Coupled Two-Way Clustering Analysis of Gene Microarray Data

@article{citeulike:2938582, abstract = {We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.}, author = {Getz, Gad and Levine, Erel and Domany, Eytan }, citeulike-article-id = {2938582}, doi = {http://dx.doi.org/10.2307/123793}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {gene-expression, microarray}, number = {22}, pages = {12079--12084}, posted-at = {2008-06-28 06:06:46}, priority = {2}, publisher = {National Academy of Sciences}, title = {Coupled Two-Way Clustering Analysis of Gene Microarray Data}, url = {http://dx.doi.org/10.2307/123793}, volume = {97}, year = {2000} }

TY - JOUR ID - citeulike:2938582 L3 - citeulike-article-id:2938582 TI - Coupled Two-Way Clustering Analysis of Gene Microarray Data JF - Proceedings of the National Academy of Sciences of the United States of America VL - 97 IS - 22 SP - 12079 EP - 12084 PB - National Academy of Sciences N2 - We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research. KW - gene-expression KW - microarray AU - Getz, Gad AU - Levine, Erel AU - Domany, Eytan PY - 2000/// UR - http://dx.doi.org/10.2307/123793 ER -