Clustering gene-expression data with repeated measurements
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* Corresponding author: Ka Y Yeung kayee@u.washington.edu
Genome Biology 2003, 4:R34 doi:10.1186/gb-2003-4-5-r34
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BioMed Central: 14 citations
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Software
R/BHC: fast Bayesian hierarchical clustering for microarray data Richard S Savage, Katherine Heller, Yang Xu, Zoubin Ghahramani, William M Truman, Murray Grant, Katherine J Denby, David L Wild BMC Bioinformatics 2009, 10:242 (6 August 2009) |
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Research article
Michael Gormley, Aydin Tozeren BMC Bioinformatics 2008, 9:486 (17 November 2008) |
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Methodology article
Partial mixture model for tight clustering of gene expression time-course Yinyin Yuan, Chang-Tsun Li, Roland Wilson BMC Bioinformatics 2008, 9:287 (18 June 2008) |
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Software
ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use Piotr Kraj, Ashok Sharma, Nikhil Garge, Robert Podolsky, Richard A McIndoe BMC Bioinformatics 2008, 9:200 (16 April 2008) |
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Methodology article
Microarray data mining using landmark gene-guided clustering Pankaj Chopra, Jaewoo Kang, Jiong Yang, HyungJun Cho, Heenam Kim, Min-Goo Lee BMC Bioinformatics 2008, 9:92 (11 February 2008) |
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Proceedings
Ivan G Costa, Roland Krause, Lennart Opitz, Alexander Schliep BMC Bioinformatics 2007, 8(Suppl 10):S3 (21 December 2007) |
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Methodology article
Gene expression trees in lymphoid development Ivan G Costa, Stefan Roepcke, Alexander Schliep BMC Immunology 2007, 8:25 (9 October 2007) |
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Research article
Including probe-level uncertainty in model-based gene expression clustering Xuejun Liu, Kevin K Lin, Bogi Andersen, Magnus Rattray BMC Bioinformatics 2007, 8:98 (21 March 2007) |
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Research article
An approach for clustering gene expression data with error information Brian Tjaden BMC Bioinformatics 2006, 7:17 (12 January 2006) |
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Method
Consensus clustering and functional interpretation of gene-expression data Stephen Swift, Allan Tucker, Veronica Vinciotti, Nigel Martin, Christine Orengo, Xiaohui Liu, Paul Kellam Genome Biology 2004, 5:R94 (1 November 2004) Consensus clustering, a new method for analyzing microarray data that takes a consensus set of clusters from various algorithms, is shown to perform better than individual methods alone. |
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Methodology article
Reuse of imputed data in microarray analysis increases imputation efficiency Ki-Yeol Kim, Byoung-Jin Kim, Gwan-Su Yi BMC Bioinformatics 2004, 5:160 (26 October 2004) |
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Research article
Alexandre G de Brevern, Serge Hazout, Alain Malpertuy BMC Bioinformatics 2004, 5:114 (23 August 2004) |
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Research
From co-expression to co-regulation: how many microarray experiments do we need? Ka Yeung, Mario Medvedovic, Roger E Bumgarner Genome Biology 2004, 5:R48 (28 June 2004) The ability to identify co-regulated genes from microarray clustering results is strongly dependent on the number of microarray experiments used in cluster analysis and the accuracy of these associations plateaus at between 50 and 100 experiments on yeast data. Even with large numbers of experiments, the false positive rate may exceed the true positive rate. |
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Software
Multiclass classification of microarray data with repeated measurements: application to cancer Ka Yeung, Roger E Bumgarner Genome Biology 2003, 4:R83 (24 November 2003) Prediction of the diagnostic category of a tissue sample from its gene-expression profile and selection of relevant genes for class prediction have important applications in cancer research. Uncorrelated shrunken centroid and error-weighted, uncorrelated shrunken centroid algorithms have been developed that are applicable to microarray data with any number of classes. |