{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T22:52:26Z","timestamp":1791327146615,"version":"4.3.1"},"reference-count":36,"publisher":"Cold Spring Harbor Laboratory","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genome Res."],"published-print":{"date-parts":[[2003,4,1]]},"abstract":"<jats:p>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 \u201cmarker genes\u201d that are differentially expressed in particular sets of \u201cconditions.\u201d We have developed a method that simultaneously clusters genes and conditions, finding distinctive \u201ccheckerboard\u201d 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).<\/jats:p>","DOI":"10.1101\/gr.648603","type":"journal-article","created":{"date-parts":[[2003,4,2]],"date-time":"2003-04-02T03:44:29Z","timestamp":1049255069000},"page":"703-716","source":"Crossref","is-referenced-by-count":544,"title":["Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions"],"prefix":"10.1101","volume":"13","author":[{"given":"Yuval","family":"Kluger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronen","family":"Basri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph T.","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Gerstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"246","published-online":{"date-parts":[[2003,4,1]]},"reference":[{"key":"2021111810480614000_13.4.703.1","doi-asserted-by":"publisher","DOI":"10.1038\/35000501"},{"key":"2021111810480614000_13.4.703.2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.97.18.10101"},{"key":"2021111810480614000_13.4.703.3","doi-asserted-by":"crossref","unstructured":"Bapat R.B. Raghavan T.E.S. (1997) Non-negative matrices and applications . Chapter 6. (Cambridge University Press, Cambridge, UK).","DOI":"10.1017\/CBO9780511529979"},{"key":"2021111810480614000_13.4.703.4","unstructured":"Ben-Hur, A., Elisseeff, A., and Guyon, I. 2002. A stability based method for discovering structure in clustered data. Pac. Symp. Biocomput.:\u00e2\u0080\u00896\u00e2\u0080\u009317.."},{"key":"2021111810480614000_13.4.703.5","doi-asserted-by":"publisher","DOI":"10.1038\/35020115"},{"key":"2021111810480614000_13.4.703.6","doi-asserted-by":"publisher","DOI":"10.1038\/4462"},{"key":"2021111810480614000_13.4.703.7","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.97.1.262"},{"key":"2021111810480614000_13.4.703.8","unstructured":"Cheng Y. Church G.M. (2000) Biclustering of expression data. 8th International Conference on Intelligent Systems for Molecular Biology, August 2000. 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