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'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Open accessAbstract
BACKGROUND: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. RESULTS: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. CONCLUSIONS: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.
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“After normalization, gene shaving (22), bootstrapping (23), and cluster analysis were performed (24), and the slide effect, dye effect, variety effect, and duplicate design were taken into account in an ANOVA model (25).”
From A High-Fat Diet Coordinately Downregulates Genes Required for Mitochondrial Oxidative Phosphorylation in Skeletal Muscle (2005) · cites this paper as [22]
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Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Self-citationGolub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Self-citationAlon, U. S., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S. E., Mack, D. H., & Levine, A. J. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.12.6745 · Cited by 4,170
Tamayo, P., Slonim, D. K., Mesirov, J. P., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., & Golub, T. R. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.2907 · Cited by 2,924
Distinctive gene expression patterns in human mammary epithelial cells and breast cancers
Perou, C. M., Jeffrey, S. S., van de Rijn, M., Rees, C. A., Eisen, M. B., Ross, D. T., Pergamenschikov, A., Williams, C. F., Zhu, S., Lee, J. C. F., et al. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.16.9212 · Cited by 1,402
Self-citationAn Information-Intensive Approach to the Molecular Pharmacology of Cancer
Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L., Anderson, N. L., et al. (1997) · Science · doi:10.1126/science.275.5298.343 · Cited by 1,183
Roth, F. P., Hughes, J. D., Estep, P. W., & Church, G. M. (1998) · Nature Biotechnology · doi:10.1038/nbt1098-939 · Cited by 939
Synexpression groups in eukaryotes
Niehrs, C., & Pollet, N. (1999) · Nature · doi:10.1038/990025 · Cited by 429
Research works printed but not linked: 2
- Tibshirani R, Hastie T, Eisen M, Ross D, Botstein D, Brown P: Clustering methods for the analysis of DNA microarray data. Technical report. Stanford: Department of Statistics, Stanford University; . 1999
- Hastie T, Tibshirani R, Eisen M, Brown P, Scherf U, Weinstein J, Alizadeh A, Staudt L, Botstein D: Gene shaving: a new class of clustering methods for expression arrays. Technical report. Stanford: Stanford University;. 2000
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(2010) · Wiley eBooks · doi:10.1002/9780470593417.refs
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Amaratunga, D., Cabrera, J., & Shkedy, Z. (2014) · Wiley series in probability and statistics · doi:10.1002/9781118364505.refs
Drăghici, S. (2003) · Humana Press eBooks · doi:10.1007/978-1-59259-335-4_35
Amaratunga, D., & Cabrera, J. (2003) · Wiley series in probability and statistics · doi:10.1002/9780470317129.refs
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Lu, Q. L., Song, Y. E., & Gray‐McGuire, C. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess7196
Albalate, A., & Minker, W. (2013) · doi:10.1002/9781118557693.biblio
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Li, Y., Bi, J., Liu, J., & Yang, Y. (2024) · Journal of Applied Statistics · doi:10.1080/02664763.2024.2348634
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Metadata
- Authors
- Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P.
- Affiliations
- Stanford University (US)
- Lawrence Berkeley National Laboratory (US)
- Stanford Medicine (US)
- National Institutes of Health (US)
- University of Nebraska Medical Center (US)
- Corresponding author
- Tibshirani, Robert
- ORCID
- Venue
- Genome Biology
- Published
- 2000-08-04
- Volume
- 1
- Issue
- 2
- Pages
- RESEARCH0003-RESEARCH0003
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC15015
- OpenAlex
- W1774711529
- Cited by
- 547
- References
- 18
- Citations by year
- 2025: 1 · 2024: 8 · 2022: 7 · 2021: 7 · 2020: 5 · 2019: 11 · 2018: 12 · 2017: 11 · 2016: 17 · 2015: 20 · 2014: 20 · 2013: 21 · 2012: 18
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Cancer Institute (U01CA084967, P01CA034233, CA 34233, UOI-CA 84967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- gene expression clustering, gene expression analysis, supervised clustering, hierarchical clustering, diffuse large B-cell lymphoma, survival prediction, gene expression signatures, microarray data
- MeSH terms
- Algorithms, Humans, RNA, Messenger, Gene Expression Regulation, Neoplastic, Cluster Analysis, Survival Analysis, Lymphoma, B-Cell, Lymphoma, Large B-Cell, Diffuse, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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