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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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Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152
“Hastie et al. (2000) proposed the so-called gene shaving techniques using PCA to cluster highly variable and coherent genes in microarray datasets.”
MethodSelf-citationMulticlass cancer diagnosis using tumor gene expression signatures
Ramaswamy, S., Tamayo, P., Rifkin, R. M., Mukherjee, S., Yeang, C., Angelo, M., Ladd, C., Reich, M. M., Latulippe, É., Mesirov, J. P., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.211566398 · Cited by 2,029
Weisenberger, D. J., Siegmund, K. D., Campan, M., Young, J. P., Long, T. I., Faasse, M. A., Kang, G. H., Widschwendter, M., Weener, D., Buchanan, D. D., et al. (2006) · Nature Genetics · doi:10.1038/ng1834 · Cited by 1,990
“We also applied a fourth marker selection algorithm (gene shavin”
Method[9] TM4 Microarray Software Suite
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Menze, B. H., Kelm, B. M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W. H., & Hamprecht, F. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-213 · Cited by 1,315
“importance, it is considering conditional higher-order interactions between the variables when measuring the importance of certain spectral regions, providing a better ranking criterion than a univariate measure used here and in similar tasks elsewhere [9,10].”
BackgroundCluster analysis for gene expression data: a survey
Jiang, D., Tang, C., & Zhang, A. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.68 · Cited by 1,238
“Another approach first groups the features into coherent sets using a clustering algorithm and then projects the samples onto a lowerdimensional space spanned by the average expression patterns of the coherent feature sets [Hastie2000].”
MethodSingular Value Decomposition and Principal Component Analysis
Wall, M. E., Rechtsteiner, A., & Rocha, L. M. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/0-306-47815-3_5 · Cited by 1,218
“, 2001) and gene shaving (Hastie et al., 2000) are published SVD-based grouping algorithms; SVDMAN is free software available at http://home.”
BackgroundMethodA Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data
Hira, Z. M., & Gillies, D. F. (2015) · Advances in Bioinformatics · doi:10.1155/2015/198363 · Cited by 978
“%) that have the smallest absolute inner product with the leading principal component [67].”
BackgroundSparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
Chun, H., & Keleş, S. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2009.00723.x · Cited by 927
“In this section, we compare spls regression to some of the popular methods to handle multicollinearity such as pls , pcr , ridge regression, mixed variance-covariance approach, gene-shaving (Hastie et al., 2000) and super pc (Bair et al., 2006).”
MethodComputational cluster validation in post-genomic data analysis
Handl, J., Knowles, J. D., & Kell, D. B. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti517 · Cited by 911
“Prominent examples include biclustering techniques [51] and gene shaving [33], which have both been specifically designed to deal with the particular challenges posed by gene expression data.”
BackgroundWu, M., Sun, L. V., Vamathevan, J., Riegler, M., Deboy, R., Brownlie, J. C., McGraw, E. A., Martin, W., Esser, C., Ahmadinejad, N., et al. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020069 · Cited by 851
Prediction by Supervised Principal Components
Bair, E., Hastie, T. J., Paul, D., & Tibshirani, R. J. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000000628 · Cited by 824
“Hastie et al. (2000) proposed “gene shaving” as a method for clustering genes.”
MethodSelf-citationSpectral Biclustering of Microarray Data: Coclustering Genes and ConditionsPreprint
Kluger, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003) · Genome Research · doi:10.1101/gr.648603 · Cited by 742
“These methods apply greedy iterative search to find interesting patterns in the matrices, an approach that is also common in one-sided clustering (Hastie et al. 2000; Stolovitzky et al. 2000).”
MethodSparse principal component analysis via regularized low rank matrix approximation
Shen, H., & Huang, J. Z. (2007) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2007.06.007 · Cited by 685
“(Hastie et al., 2000) and meta-genes (West, 2003).”
MethodSparks, L. M., Xie, H., Koza, R. A., Mynatt, R., Hulver, M. W., Bray, G. A., & Smith, S. R. (2005) · Diabetes · doi:10.2337/diabetes.54.7.1926 · Cited by 629
“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).”
MethodDrăghici, S., Khatri, P., Martins, R. P., Ostermeier, G. C., & Krawetz, S. A. (2003) · Genomics · doi:10.1016/s0888-7543(02)00021-6 · Cited by 584
From patterns to pathways: gene expression data analysis comes of age
Slonim, D. K. (2002) · Nature Genetics · doi:10.1038/ng1033 · Cited by 576
“So-called 'fuzzy' clustering finds groups, but may allow elements to appear in more than one cluster or in no clusters at al”
BackgroundA mixture model-based approach to the clustering of microarray expression data
McLachlan, G. J., Bean, R. W., & Peel, D. A. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.3.413 · Cited by 559
“…validation, and understanding of various classes and subclasses of cancer; see, for example, Eisen et al. (1998), Ben-Dor et al. (1999) (2000), Alon et al. (1999), Golub et al. (1999), Hastie et al. (2000), Moler et al. (2000), Nguyen and Rocke (2001), and Xing and Karp (2001), among others.”
BackgroundGeneralized Thresholding of Large Covariance Matrices
Rothman, A. J., Levina, E., & Zhu, J. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.0101 · Cited by 495
“Clustering genes using their correlations is a popular technique in gene expression data analysis (Eisen et al. 1998; Hastie et al. 2000).”
MethodBootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments
Kerr, M. K., & Churchill, G. A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.161273698 · Cited by 474
Statistical Issues in cDNA Microarray Data Analysis
Smyth, G. K., Yang, J. Y. H., & Speed, T. P. (2003) · Humana Press eBooks · doi:10.1385/1-59259-364-x:111 · Cited by 471
“Recent work includes that of Hastie et al [56] who form clusters around the largest principal components of the data, Lazzeroni and Owen [57] who propose models in which each gene can belong to a more than one cluster to none at all as different characteristics are considered, Parmigiani et al [58] who consider more g…”
MethodMirnics, K., Middleton, F. A., Lewis, D. A., & Levitt, P. R. (2001) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01862-2 · Cited by 395
EXTRACTING CONSERVED GENE EXPRESSION MOTIFS FROM GENE EXPRESSION DATA
Murali, T. M., & Kasif, S. (2002) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812776303_0008 · Cited by 385
“Finally, Hastie et al. 17 present a technique called \gene shaving" that tries to extract coherent and small clusters of genes that vary as much as possible across the samples; in essence, their goals are complementary to ours.”
BackgroundNovel Unsupervised Feature Filtering of Biological Data
Varshavsky, R., Gottlieb, A., Michal Linial, B., & Horn, D. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl214 · Cited by 339
“The latter is a variation of a method of Hastie et al .”
MethodDefining transcription modules using large-scale gene expression data
Ihmels, J., Bergmann, S., & Barkai, N. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth166 · Cited by 338
“results when applied to relatively small datasets, their utility for the analysis of large datasets appears to be limited by several drawbacks (Bittner et al., 1999; Cheng and Church, 2000; Gasch and Eisen, 2002; Getz et al., 2000; Hastie et al., 2000; Tanay et al., 2002).”
BackgroundMethodLinear modes of gene expression determined by independent component analysis
Liebermeister, W. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.1.51 · Cited by 336
“Gene shaving (Hastie et al., 2000) determines optimal”
Jiang, H., Deng, Y., Chen, H., Lin, T., Sha, Q., Chen, J., Tsai, C., & Zhang, S. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-81 · Cited by 294
“To aid marker gene identification, we have developed GSRF and GSFLD methods by combining the idea of gene shaving, an unsupervised classification [13], with the supervised classification techniques, RF [8,9] and FLD [14].”
MethodKernel Methods and Machine Learning
Kung, S. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139176224 · Cited by 289
“A “gene shaving” approach [93] in one which seeks subsets of genes with large variations across samples (or conditions) that have similar expression patterns by iteratively discarding the genes with the smallest variations was proposed.”
BackgroundOpen Source Software for the Analysis of Microarray Data
Dudoit, S., Gentleman, R. C., & Quackenbush, J. F. (2003) · BioTechniques · doi:10.2144/mar03dudoit · Cited by 282
Howe, E., Holton, K. M., Nair, S. J., Schlauch, D., Sinha, R., & Quackenbush, J. F. (2010) · Springer eBooks · doi:10.1007/978-1-4419-5714-6_15 · Cited by 270
Cluster and Classification Techniques for the Biosciences
Fielding, A. H. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511607493 · Cited by 266
A new variable selection approach using Random Forests
Hapfelmeier, A., & Ulm, K. (2012) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2012.09.020 · Cited by 267
CpG Island Methylation of DNA Damage Response Genes in Advanced Ovarian Cancer
Teodoridis, J. M., Hall, J. A., Marsh, S., Kannall, H. D., Smyth, C., Curto, J., Siddiqui, N. A., Gabra, H., McLeod, H. L., Strathdee, G. R., et al. (2005) · Cancer Research · doi:10.1158/0008-5472.can-05-1187 · Cited by 252
“To examine concordant methylation in the current study, we have used a bioinformatics approach, gene shaving (26), which provides an objective approach to identify coherent patterns of methylation.”
MethodDetecting Stable Clusters Using Principal Component Analysis
Ben‐Hur, A., & Guyon, I. (2003) · Humana Press eBooks · doi:10.1385/1-59259-364-x:159 · Cited by 252
“This is also the basis of the “gene shaving” method (24) that builds a set of variables by iteratively discarding var iables that are least correlated with the leading principal components.”
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Menze, B. H., Kelm, B. M., Splitthoff, D. N., Köthe, U., & Hamprecht, F. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_29 · Cited by 234
“We also compare both random forest scores with a t-test measuring class separation individually at every feature – similar to, for example, fMRI experiments, microarray, or tests on spectra [15].”
MethodPittman, J. L., Huang, E. S., Dressman, H. K., Horng, C., Cheng, S. H., Tsou, M.-H., Chen, C.-M., Bild, A., Iversen, E. S., Huang, A. T., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0401736101 · Cited by 218
Supervised harvesting of expression trees
Hastie, T. J., Tibshirani, R. J., Botstein, D., & Brown, P. O. (2001) · Genome biology · doi:10.1186/gb-2001-2-1-research0003 · Cited by 222
Self-citationA Cross-Study Comparison of Gene Expression Studies for the Molecular Classification of Lung Cancer
Parmigiani, G., Garrett-Mayer, E. S., Anbazhagan, R., & Gabrielson, E. W. (2004) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-03-0490 · Cited by 209
Model-based overlapping clustering
Banerjee, A., Krumpelman, C. S., Ghosh, J., Basu, S., & Mooney, R. J. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081932 · Cited by 210
“Several methods for obtaining ov erlapping gene clusters, including gene shaving [20] and mean squ are residue bi-clustering [10] have been proposed.”
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“…proposed based on projections of the data after performing singular value decomposition on the expression matrix to identify the eigenvectors (or eigengenes) as representative expression profiles (see Hastie et al. 2000; Holter, Maritan, Marek, Fedoroff, and Banavar 2001, and references therein).”
MethodWolf, L., & Shashua, A. (2005) · doi:10.5555/1046920.1194906 · Cited by 207
Hoang, S. A., Oseini, A. M., Feaver, R. E., Cole, B. K., Asgharpour, A., Vincent, R. J., Siddiqui, M. S., Lawson, M. J., Day, N., Taylor, J. M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48746-5 · Cited by 195
Adaptive quality-based clustering of gene expression profiles
Smet, F. D., Mathys, J., Kathleen Marchal, D., Thijs, G., Moor, B. D., & Moreau, Y. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.5.735 · Cited by 189
“, 1999) and gene shaving (Hastie et al., 2000) were developed (gene shaving also uses a quality measure to select the cluster size). Heyer et al. (1999) proposed an algorithm (QT Clust) that tries to identify clusters that have a certain quality (representing the minimal degree of coexpression needed—see below for the…”
BackgroundMethodInfluentialAnalyzing microarray data using cluster analysis
Shannon, W. D., Culverhouse, R., & Duncan, J. (2003) · Pharmacogenomics · doi:10.1517/phgs.4.1.41.22581 · Cited by 187
“For example, one method to determine the number of clusters without resorting to external criteria is to use the number that optimizes the Gap statistic, a statistic comparing within-cluster dispersion (spread of data points) to dispersion under the null hypothesis [12,19].”
MethodA Selective Overview of Sparse Principal Component Analysis
Zou, H., & Xue, L. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2846588 · Cited by 188
“As a dimension reduction and feature extraction method, PCA has numerous applications in statistical learning, such as handwritten zip code classification [26], human face recognition [24], eigengenes analysis [1], gene shaving [25], and so on.”
MethodUnsupervised strategies for shilling detection and robust collaborative filtering
Mehta, B., & Nejdl, W. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9050-4 · Cited by 170
“…Shaving 3 z-scores can be computed for a user u for a item y, where the user has voted vu,y , by using the equation zu,y = vu,y − v̂u σu , (9) technique (Hastie et al. 2000) is such a technique in use for clustering highly correlated genes from a set of observed data, which includes missing values.”
MethodFinding large average submatrices in high dimensional data
Shabalin, A. A., Weigman, V., Perou, C. M., & Nobel, A. B. (2009) · The Annals of Applied Statistics · doi:10.1214/09-aoas239 · Cited by 166
“The simplest, and most common, way of identifying associations in gene expression data is to independently cluster the rows and columns of the data matrix using a multivariate clustering procedure [Weinstein et al. (1997); Eisen et al. (1998); Tamayo et al. (1999); Hastie et al. (2000)].”
MethodInfluentialImproved biclustering of microarray data demonstrated through systematic performance tests
Turner, H., Bailey, T., & Krzanowski, W. J. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.02.003 · Cited by 166
“These include gene-shaving (Hastie et al., 2000), a one-way overlapping clustering method; context-speci4c Bayesian clustering (Barash and Friedman, 2002), a one-way clustering method in which clusters may be based on di6erent subsets of samples; EMMIX-GENE (McLachlan et al.”
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Dettling, M., & Bühlmann, P. (2002) · Genome biology · doi:10.1186/gb-2002-3-12-research0069 · Cited by 164
“Finally, X Ci � R denotes a ‘representative’ expression value of gene cluster Ci. There are many possibilities to determine such group values XCi , but as we would like to shape clusters that contain similar genes, a simple linear combination is an accurate choice (see [5, 10 ]):”
BackgroundCorrelated variables in regression: Clustering and sparse estimation
Bühlmann, P., Rütimann, P., van de Geer, S., & Zhang, C. (2013) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2013.05.019 · Cited by 155
“Principal component regression (Kendall, 1957, cf.) is among the earliest proposals, and Hastie et al. (2000) have used principal component analysis in order to find a set of highly correlated variables where the clustering can be supervised by a response variable.”
MethodEvolving clusters in gene-expression data
Hruschka, E. R., Campello, R. J. G. B., & de Castro, L. N. (2005) · Information Sciences · doi:10.1016/j.ins.2005.07.015 · Cited by 157
Developing specific molecular biomarkers for thermal stress in salmonids
Akbarzadeh, A., Günther, O. P., Houde, A. L. S., Li, S., Ming, T. J., Jeffries, K. M., Hinch, S. G., & Miller, K. M. (2018) · BMC Genomics · doi:10.1186/s12864-018-5108-9 · Cited by 164
“Shown is a heatmap (left) and PCA plot (right) of 21 sockeye salmon samples (2007) for 98 features returned by Gene Shaving applied to the filtered 2007 sockeye salmon 30,072-feature data set.”
MethodInfluentialMehta, B., Hofmann, T., & Fankhauser, P. (2007) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1216295.1216307 · Cited by 145
“The Gene Shaving technique[ 3 ] is such a technique in use for clustering highl y correlated genes from a set of observed data, which includes missing values.”
MethodPartial Cox regression analysis for high-dimensional microarray gene expression data
Li, H., & Gui, J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth900 · Cited by 144
“Perhaps the most developed technique in relating gene expression profiles to phenotypes is the gene harvesting procedure of Hastie et al. (2001). This is a forward stepwise regression method that can be applied to a spectrum of outcome types, including survival data, in which case the stepwise regression corresponds t…”
MethodInfluentialA Statistical Framework for Expression-Based Molecular Classification in Cancer
Parmigiani, G., Garrett, E. S., Anbazhagan, R., & Gabrielson, E. W. (2002) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00358 · Cited by 139
Pattern Recognition Techniques in Microarray Data Analysis
Valafar, F. (2002) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2002.tb04888.x · Cited by 141
Discriminating Different Classes of Toxicants by Transcript Profiling
Steiner, G., Suter‐Dick, L., Boess, F., Gasser, R., de Vera, M. C., Albertini, S., & Ruepp, S. U. (2004) · Environmental Health Perspectives · doi:10.1289/txg.7036 · Cited by 150
“A proposed method to discriminate the most relevant gene changes from background biological and experimental variation is gene shaving (Hastie et al. 2000).”
MethodClassification and Selection of Biomarkers in Genomic Data Using LASSO
Ghosh, D., & Chinnaiyan, A. M. (2005) · BioMed Research International · doi:10.1155/jbb.2005.147 · Cited by 126
“Linear discriminant functions by optimal scoring While linear discriminant analysis (LDA) is typically calculated using matrix algebra techniques, an alternative method of calculating them is through the use of optimal scoring (Hastie et al [16, 17]).”
MethodFTIR spectroscopy of biofluids revisited: an automated approach to spectral biomarker identification
Ollesch, J., Drees, S. L., Heise, H. M., Behrens, T., Brüning, T., & Gerwert, K. B. (2013) · The Analyst · doi:10.1039/c3an00337j · Cited by 119
Statistical Challenges in Functional Genomics
Sebastiani, P., Gussoni, E., Kohane, I. S., & Ramoni, M. F. (2003) · Statistical Science · doi:10.1214/ss/1056397486 · Cited by 123
“Clustering methods that address the issue of simultaneously sorting rows and columns of the matrix Y have been proposed, such as gene shaving (Hastie et al., 2000), biclustering (Cheng and Church, 2000), coupled two-way clustering (Getz, Levine and Domany, 2000) and the plaid model (Lazzeroni and Owen, 2002).”
MethodComparative genomics of the genus Bifidobacterium
Bottacini, F., Medini, D., Pavesi, A., Turroni, F., Foroni, E., Riley, D. R. J., Giubellini, V., Tettelin, H., van Sinderen, D., & Ventura, M. (2010) · Microbiology · doi:10.1099/mic.0.039545-0 · Cited by 120
The Enhanced Suffix Array and Its Applications to Genome Analysis
Abouelhoda, M. I., Kurtz, S., & Ohlebusch, E. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45784-4_35 · Cited by 119
Comparison of microarray designs for class comparison and class discovery
Dobbin, K. K., & Simon, R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.11.1438 · Cited by 118
“Microarray data are also used in unsupervised clustering to determine whether genetically diverse subgroups exist in collections of samples (Hastie et al., 2000; Ben-Dor et al., 2000).”
MethodModulation of Gene Expression by Tumor-Derived p53 Mutants
Scian, M. J., Stagliano, K. E., Ellis, M. A., Hassan, S., Bowman, M., Miles, M. F., Deb, S. P., & Deb, S. (2004) · Cancer Research · doi:10.1158/0008-5472.can-04-1568 · Cited by 117
Talloen, W., Clevert, D.-A., Hochreiter, S., Amaratunga, D., Bijnens, L., Kass, S. U., & Göhlmann, H. W. H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm478 · Cited by 117
Diametrical clustering for identifying anti-correlated gene clusters
Dhillon, I. S., Marcotte, E. M., & Roshan, U. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg209 · Cited by 111
Statistical intelligence: effective analysis of high-density microarray data
Drăghici, S. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02292-4 · Cited by 110
Tseng, G. C. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm320 · Cited by 106
Microarrays: handling the deluge of data and extracting reliable information
Hess, K. R., Zhang, W., Baggerly, K., Stivers, D. N., Coombes, K. R., & Zhang, W. (2001) · Trends in biotechnology · doi:10.1016/s0167-7799(01)01792-9 · Cited by 107
An augmented Lagrangian approach for sparse principal component analysis
Lu, Z., & Zhang, Y. (2011) · Mathematical Programming · doi:10.1007/s10107-011-0452-4 · Cited by 105
“†Department of Mathematics, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.”
Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect
O’Neill, M. C., & Li, S. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-13 · Cited by 102
New Approaches to Lymphoma Diagnosis
Harris, N. L., Stein, H., Coupland, S. E., Hummel, M., Favera, R. D., Pasqualucci, L., & Chan, W. C. (2001) · Hematology · doi:10.1182/asheducation-2001.1.194 · Cited by 102
Self-citationSerban, N., & Wasserman, L. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001574 · Cited by 101
“The set B̃i = {θ̃ : θi ∈ Bi} (13) is a confidence set for θ̃ and is a compact set (B̃i = f(Bi)).”
BackgroundHigdon, R., Earl, R. K., Stanberry, L. I., Hudac, C. M., Montague, E., Stewart, E., Janko, I., Choiniere, J., Broomall, W., Kolker, N., et al. (2015) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2015.0020 · Cited by 107
“Other methods limit the number of feature or genes used for clustering, creating both tighter and easier to interpret clusters; among these are sparse clustering, gene shaving, and dendogram sharpening (Hastie et al., 2000; Stanberry et al., 2013, 2003; Witten and Tibshirani, 2010).”
SVDMAN—singular value decomposition analysis of microarray data
Wall, M. E., Dyck, P. A., & Brettin, T. S. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.566 · Cited by 99
“Finally we contrast SVDMAN with two other analysis methods: clustering (see, e.g. Eisen et al. , 1998) and gene shaving (Hastie et al. , 2000).”
MethodThe Analysis of Gene Expression Data: An Overview of Methods and Software
Parmigiani, G., Garrett, E. S., Irizarry, R. A., & Zeger, S. L. (2003) · Statistics for biology and health · doi:10.1007/0-387-21679-0_1 · Cited by 96
“See Herrero et al. (2001) or Segal et al.”
BackgroundLi, X. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh563 · Cited by 96
Duke, D. C., Moran, L. B., Kalaitzakis, M. E., Deprez, M., Dexter, D. T., Pearce, R. K. B., & Graeber, M. B. (2006) · Neurogenetics · doi:10.1007/s10048-006-0033-5 · Cited by 93
“As an eigenvector is a linear combination of all gene expression values, only the probes that contributed significantly to the eigenvector were of interest and were selected on the basis of the “Gap” test [37].”
MethodOllesch, J., Heinze, M., Heise, H. M., Behrens, T., Brüning, T., & Gerwert, K. B. (2014) · Journal of Biophotonics · doi:10.1002/jbio.201300163 · Cited by 93
Elucidating endometrial function in the post-genomic era
Giudice, L. C. (2003) · Human Reproduction Update · doi:10.1093/humupd/dmg019 · Cited by 92
“Unsupervised clustering analyses permit an unbiased discovery of expression periodicity (i.e. regularly recurring gene expression under certain conditions) and patterns of genes in tissues that do not require prior segregation into different groups (Eisen et al., 1998; Hastie et al., 2000).”
Covell, D. G., Wallqvist, A., Rabow, A. A., & Thanki, N. (2003) · PubMed · Cited by 87
Selection for Chromosome Architecture in Bacteria
Hendrickson, H. L., & Lawrence, J. G. (2006) · Journal of Molecular Evolution · doi:10.1007/s00239-005-0192-2 · Cited by 86
Dimension reduction strategies for analyzing global gene expression data with a response
Chiaromonte, F., & Martinelli, J. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(01)00106-7 · Cited by 82
Feig, C., Kirchhoff, C., Ivell, R., Naether, O., Schulze, W., & Spiess, A. (2006) · Molecular Human Reproduction · doi:10.1093/molehr/gal097 · Cited by 81
“The latter method is somewhat similar to the 'gene shaving' method described by Hastie et al. (2000), but we used the deviation of the first principle component as the measure instead of the inner product to the first principle component.”
MethodSzabó, A., Boucher, K. M., Carroll, W. L., Klebanov, L. B., Tsodikov, A. D., & Yakovlev, A. Y. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(01)00103-1 · Cited by 81
“[16,17] have attempted to use clusters in prediction, but their method of averaging the readings over the genes included in a given cluster seems to disregard a substantial part of the multidimensional information contained in sample observations.”
BackgroundMethodMolecular indices of viral disease development in wild migrating salmon†
Miller, K. M., Günther, O. P., Li, S., Kaukinen, K. H., & Ming, T. J. (2017) · Conservation Physiology · doi:10.1093/conphys/cox036 · Cited by 80
“shaving algorithm which provides an automatic feature selection by applying principle component analysis (PCA) iteratively to ‘shave off’ genes and create a sequence of clusters with different sizes (Hastie et al., 2000).”
MethodApplications of Bayesian network models in predicting types of hematological malignancies
Agrahari, R., Foroushani, A. B. K., Docking, R., Chang, L., Duns, G., Hudoba, M., Karsan, A., & Zare, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-24758-5 · Cited by 79
Overview of Commonly Used Bioinformatics Methods and Their Applications
Kapetanović, I. M., Rosenfeld, S., & Izmirlian, G. (2004) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1310.003 · Cited by 76
The liver pharmacological and xenobiotic gene response repertoire
Natsoulis, G., Pearson, C. I., Gollub, J., Eynon, B. P., Ferng, J., Nair, R. V., Idury, R., Lee, M. D., Fielden, M. R., Brennan, R., et al. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.9 · Cited by 77
“The liver pharmacological and xenobiotic gene response repertoire”
BackgroundMethodNew modifications and applications of fuzzy -means methodology
Berget, I., Mevik, B., & Næs, T. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.10.020 · Cited by 76
Bayesian ensemble methods for survival prediction in gene expression data
Bonato, V., Baladandayuthapani, V., Broom, B. M., Sulman, E. P., Aldape, K., & Do, K.-A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq660 · Cited by 74
Development and validation of biomarker classifiers for treatment selection
Simon, R. M. (2007) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2007.06.010 · Cited by 73
“The method of gene shaving attempts to provide linear combinations with properties similar to the principal components that does not require measuring all of the genes (Hastie et al., 2000).”
MethodPermutation-validated principal components analysis of microarray data
Landgrebe, J., Wurst, W., & Welzl, G. (2002) · Genome biology · doi:10.1186/gb-2002-3-4-research0019 · Cited by 73
Bhaumik, R., Jenkins, L. M., Gowins, J. R., Jacobs, R. H., Barba, A., Bhaumik, D. K., & Langenecker, S. A. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.02.018 · Cited by 72
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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