Spectral biclustering of microarray data: coclustering genes and conditions.

@article{citeulike:79882, abstract = {Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).}, address = {Department of Genetics, Yale University, New Haven, Connecticut 06520, USA.}, author = {Kluger, Y. and Basri, R. and Chang, J. T. and Gerstein, M. }, citeulike-article-id = {79882}, doi = {10.1101/gr.648603}, issn = {1088-9051}, journal = {Genome Res}, keywords = {biclustering, data, microarray, of, spectral}, month = {April}, number = {4}, pages = {703--716}, posted-at = {2005-12-06 20:42:02}, priority = {5}, title = {Spectral biclustering of microarray data: coclustering genes and conditions.}, url = {http://dx.doi.org/10.1101/gr.648603}, volume = {13}, year = {2003} }

TY - JOUR ID - citeulike:79882 L3 - citeulike-article-id:79882 TI - Spectral biclustering of microarray data: coclustering genes and conditions. JF - Genome Res VL - 13 IS - 4 SP - 703 EP - 716 AD - Department of Genetics, Yale University, New Haven, Connecticut 06520, USA. SN - 1088-9051 N2 - Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data). KW - biclustering KW - data KW - microarray KW - of KW - spectral AU - Kluger, Y AU - Basri, R AU - Chang, JT AU - Gerstein, M PY - 2003/04// UR - http://dx.doi.org/10.1101/gr.648603 ER -