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'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
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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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BackgroundCross System Personalization: Enabling personalization across multiple systems
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Computation of Recommender System Using Localized Regularization
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“connected in a variety of other ways, such as similar expression patterns [10], participating in the same biology pathway, carrying out similar biological process, contributing to the same phenotype, sharing a common motif, etc.”
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“It is well recognized that, more often than not, only a very small number of sequences in a large data set may be similar to each other (Hastie et al. 2000; Dettling and Buhlmann 2002).”
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“Gene shaving [ 6 ] is an established method for generating such clusters.”
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Microarray data analysis with PCA in a DBMS
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BackgroundDistribution-free detection of a submatrix
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“The dissimilarities can potentially also be used for simultaneous feature and sample selection from the data set by backward feature elimination, in a manner similar, for example, to the “gene shaving” [Hastie et al. (2000)] and “recursive feature elimination” [Guyon et al. (2002)] procedures.”
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Galimberti, G., & Soffritti, G. (2006) · Springer eBooks · doi:10.1007/3-540-31314-1_20 · Cited by 4
Clustering gene expression profile data by selective shrinkage
Ishwaran, H., & Rao, J. S. (2008) · Statistics & Probability Letters · doi:10.1016/j.spl.2008.01.003 · Cited by 4
“For example, see the Clustering Objects on Subsets of Attributes (COSA) algorithm of Friedman and Meulman (2004) or the gene shaving algorithm of Hastie et al. (2000).”
MethodApplication of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data
Do, K.-A., McLachlan, G. J., Bean, R. W., & Wen, S. (2007) · Cancer Informatics · doi:10.1177/117693510700500002 · Cited by 4
“For example, with gene-shaving, the number of permutations needed and the percent shaving have been studied by Hastie et al. (2000).”
BackgroundMethodPredicting the survival time for diffuse large B-cell lymphoma using microarray data
Khoshhali, M., Mahjub, H., & Saidijam, M. (2012) · Journal of Molecular and Genetic Medicine · doi:10.4172/1747-0862.1000051 · Cited by 4
Identification of Tuberculosis Patient Characteristics Using K-Means Clustering
-, B. N. S. (2016) · Scientific Journal of Informatics · doi:10.15294/sji.v3i2.7909 · Cited by 4
“There are three kinds of cluster variance,ie within variance (VW), between variance (VB),and the total variance (VT)[8].”
BackgroundSnomad: Biologist-Friendly Web Tools for the Standardization and NOrmalization of Microarray Data
Colantuoni, C., Henry, G., Bouton, C. M. L. S., Zeger, S. L., & Pevsner, J. (2003) · Statistics for biology and health · doi:10.1007/0-387-21679-0_9 · Cited by 4
“... as for input into other algorithms such as clustering of genes or expression profiles (Chu et al., 1998; Eisen et al., 1998), self-organizing maps (Toronen et al., 1999), relevance networks (Butte et al., 2000), support vector machines (Brown et al., 2000), principal components analysis (Alter et al., 2000; Holter…”
Statistical Analysis of Multiplex Brain Gene Expression Images
Ossadtchi, A. E., Brown, V. M., Khan, A. H., Cherry, S. R., Nichols, T. E., Leahy, R. M., & Smith, D. (2002) · Neurochemical Research · doi:10.1023/a:1020965107124 · Cited by 4
Selecting Relevant Genes with a Spectral Approach
Wolf, L., Shashua, A., & Mukherjee, S. (2004) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 4
Valdivia-Granda, W. (2003) · Springer eBooks · doi:10.1007/978-1-4419-8760-0_8 · Cited by 4
Feature Shaving for Spectroscopic Data
Verzakov, S., Paclı́k, P., & Duin, R. P. W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27868-9_113 · Cited by 4
Virtual Gene: A Gene Selection Algorithm for Sample Classification on Microarray Datasets
Xu, X. M., & Zhang, A. (2005) · Lecture notes in computer science · doi:10.1007/11428848_131 · Cited by 3
“Techniques like PCA and SVD have been used to create salient features [6, 7] for sample classification on microarray datasets.”
MethodSparse principal components by semi-partition clustering
Enki, D. G., & Trendafilov, N. T. (2011) · Computational Statistics · doi:10.1007/s00180-011-0280-2 · Cited by 3
“Gene-shaving (Hastie et al. 2000) is such a method aimed at identifying subsets of genes with coherent expression patterns and large variation across samples.”
MethodIdentifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets
Ghebreab, S., & Smeulders, A. W. M. (2010) · Pattern Analysis and Applications · doi:10.1007/s10044-010-0186-6 · Cited by 3
Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters
Ghosh, J., & Gupta, G. (2011) · Intelligent systems reference library · doi:10.1007/978-3-642-23166-7_7 · Cited by 3
Variable selection in high-dimensional extremile regression via the quasi elastic net
Xiong, Y., Zheng, Z., & Zhang, W. (2022) · JUSTC · doi:10.52396/justc-2022-0099 · Cited by 3
Time Course Gene Expression Classification with Time Lagged Recurrent Neural Network
Liang, Y., & Kelemen, A. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-76803-6_5 · Cited by 3
“The dynamic patterns of genes expressed under different conditions can be useful indicators about gene state-trajectories and may reveal possible states and trajectories of disease and treatment effects [2–8].”
BackgroundSED, a normalization free method for DNA microarray data analysis
Wang, H., & Huang, H. L. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-121 · Cited by 3
“Depending on the algorithm, a better classification result can sometimes be obtained by using a subset of genes [ 22 ,23].”
Method
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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