The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index.
'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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 201–300 of 545
Network motif-based identification of breast cancer susceptibility genes
Zhang, Y., Xuan, J., Reyes, B. G. D. L., Clarke, R. C., & Ressom, H. W. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4650507 · Cited by 29
A clustering approach to interpretable principal components
Enki, D. G., Trendafilov, N. T., & Jolliffe, I. T. (2012) · Journal of Applied Statistics · doi:10.1080/02664763.2012.749846 · Cited by 27
“In addition, the ‘gene shaving’ algorithm [5], which deals with clustering of genes with similar expression, involves discarding (‘shaving’) a given proportion of genes having the smallest absolute correlation with the leading PC.”
BackgroundClinical Uses of Microarrays in Cancer Research
Virtanen, C., & Woodgett, J. R. (2008) · Methods in molecular medicine · doi:10.1007/978-1-60327-148-6_6 · Cited by 28
“Other popular supervised data mining techniques include “gene-shaving”(70), which can be also used in an unsupervised fashion, and “weighted-voting”, which was first introduced by Golub (16).”
MethodImpedimetric Detection of DNA Hybridization: Towards Near-Patient DNA Diagnostics
Guiseppi‐Elie, A., & Lingerfelt, L. (2005) · Springer eBooks · doi:10.1007/128_006 · Cited by 29
Sun, N., Ma, L., Pan, D., Zhao, H., & Deng, X. W. (2003) · Plant Molecular Biology · doi:10.1023/b:plan.0000019071.12878.9e · Cited by 28
“…al., 2002) or clustering genes based on expression profiles over different experimental conditions (Eisen et al., 1998; Den-Dor and Yakhini, 1999; Hastie et al., 2000; Lazzeroni and Owen, 2002) cannot relate gene expression data to the regulatory potential of any given gene in cellular metabolic…”
BackgroundAttur, M. G., Dave, M. N., Tsunoyama, K., Akamatsu, M., Kobori, M., Miki, J., Abramson, S. B., Katoh, M., & Amin, A. R. (2002) · Current Issues in Molecular Biology · doi:10.21775/cimb.004.129 · Cited by 27
“There are some reports which address these issues (Eisen et al., 1998; Rivera et al., 1998; Bassett et al., 1999; Hastie et al., 2000).”
Peng, B., Zhu, D., Ander, B. P., Zhang, X., Xue, F., Sharp, F. R., & Yang, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067672 · Cited by 28
“The screening process may also allow investigators’ subjective preferences and other methods such as the topological analysis of coexpression network [64] and bagged gene shaving [65].”
MethodCluster analysis of genome-wide expression data for feature extraction
Lin, K.-S., & Chien, C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.068 · Cited by 27
Okinaka, R. T., Price, E. P., Wolken, S. R., Gruendike, J., Chung, W. K., Pearson, T., Xie, G. G., Munk, C., Hill, K. K., Challacombe, J. F., et al. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-477 · Cited by 27
Adjaye, J. (2004) · Reproduction Fertility and Development · doi:10.1071/rd04075 · Cited by 27
“…flat classifications and clusters based on distance metrics for similarity (the specified K-value determines the number of clusters created; Herwig et al. 1999); (3) self-organising maps (SOMs), which are algorithms used to visualise and interpret large datasets in an unsupervised manner,…”
MethodIterative class discovery and feature selection using Minimal Spanning Trees
Varma, S., & Simon, R. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-126 · Cited by 27
Measuring similarities between gene expression profiles through new data transformations
Kim, K., Zhang, S., Jiang, K., Cai, L., Lee, I., Feldman, L. J., & Huang, H. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-29 · Cited by 27
“Using the gap statistic [24,25], TransChisq was also found to be advantageous in estimating the number of clusters.”
MethodBrown, N. M., Mueller, R., Shepardson, J. W., Landry, Z., Morré, J. T., Maier, C. S., Hardy, F. J., & Dreher, T. W. (2016) · BMC Genomics · doi:10.1186/s12864-016-2738-7 · Cited by 26
An overview of statistical decomposition techniques applied to complex systems
Tuncer, Y., Tanik, M. M., & Allison, D. B. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.09.012 · Cited by 26
“PCA does not seem to have lost its attractiveness despite prevailing high-dimensionality problems observed recently (Hastie et al., 2000).”
BackgroundGranular Fuzzy Possibilistic C-Means Clustering approach to DNA microarray problem
Truong, H. Q., Ngo, L. T., & Pedrycz, W. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.06.019 · Cited by 26
A Local Chaining Algorithm and Its Applications in Comparative Genomics
Abouelhoda, M. I., & Ohlebusch, E. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39763-2_1 · Cited by 26
Tailored Aggregation for Classification
Mary‐Huard, T., & Robin, S. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.55 · Cited by 26
“We present here the basics of the CART algorithm; the reader may consult Hastie et al. [20] for a complete description.”
MethodInfluentialPan, J. Z., Jörnsten, R. J., & Hart, R. P. (2004) · Physiological Genomics · doi:10.1152/physiolgenomics.00177.2003 · Cited by 26
“The gap statistic is used to determine appropriate cluster size (24).”
MethodInfluentialSparse regression and marginal testing using cluster prototypes
Reid, S., & Tibshirani, R. J. (2015) · Biostatistics · doi:10.1093/biostatistics/kxv049 · Cited by 25
“Examples of the former include principal component regression, gene shaving of Hastie et al. (2000), tree harvesting of Hastie et al. (2001) and the canonical correlation clustering and subsequent sparse regression of Buhlmann et al. (2013).”
MethodSelf-citationStatistical methods for microarray assays.
Krajewski, P., & Bocianowski, J. (2002) · PubMed · Cited by 25
Transcriptional Response to Corticotropin-Releasing Factor in AtT-20 Cells
Peeters, P. J., Göhlmann, H. W. H., den Wyngaert, I. V., Swagemakers, S. M. A., Bijnens, L., Kass, S. U., & Steckler, T. (2004) · Molecular Pharmacology · doi:10.1124/mol.104.000950 · Cited by 25
“The clear separation of the time and CRF exposure effect was not observed in the CFA, even when looking at all principal components, demonstrating a clear difference between the algorithms.”
BackgroundMethodInfluentialLearning Genetic Regulatory Network Connectivity from Time Series Data
Barker, N., Myers, C. J., & Kuwahara, H. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2009.48 · Cited by 24
“extend traditional clustering methods [43].”
Recommendation System Based on Complete Personalization
Modarresi, K. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.379 · Cited by 25
Functional genomics approaches to understanding brain disorders
Shilling, P. D., & Kelsoe, J. R. (2002) · Pharmacogenomics · doi:10.1517/14622416.3.1.31 · Cited by 24
McMaster University Data-Mining and Docking Competition: Computational Models on the Catwalk
Parker, C. N. (2005) · SLAS DISCOVERY · doi:10.1177/1087057105281268 · Cited by 24
“PUTER MODELING is having a profo nging from algorithms that help asse ods that compare the genomes of w being used to describe phenomena interactions(3) or the flux through tions are even being used to descri of neurons to environmental stimul putational analysis is also starting roughput screening (HTS), with m prove…”
MethodSolution path clustering with adaptive concave penalty
Marchetti, Y., & Zhou, Q. (2014) · Electronic Journal of Statistics · doi:10.1214/14-ejs934 · Cited by 23
Differential and trajectory methods for time course gene expression data
Liang, Y., Tayo, B. O., Cai, X., & Kelemen, A. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti465 · Cited by 23
FReM – Scalable and stable decoding with fast regularized ensemble of models
Hoyos-Idrobo, A., Varoquaux, G. P., Schwartz, Y., & Thirion, B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.005 · Cited by 23
“However, the high dimensionality of the problem leads to multiple weight maps yielding the same predictive power, and some form of regularization has to be applied (Hastie et al., 2000).”
BackgroundCraig, P., Kennedy, J., & Cumming, A. (2005) · Information Visualization · doi:10.1057/palgrave.ivs.9500101 · Cited by 23
Regulatory sequence analysis: application to the interpretation of gene expression
Vilo, J., & Kivinen, K. (2001) · European Neuropsychopharmacology · doi:10.1016/s0924-977x(01)00117-1 · Cited by 23
Autoimmune profiling with protein microarrays in clinical applications
Abel, L., Kutschki, S., Turewicz, M., Eisenacher, M., Stoutjesdijk, J., Meyer, H. E., Woitalla, D., & May, C. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.02.023 · Cited by 22
Zhao, H., Chan, K. L., Cheng, L. M., & Yan, H. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.03.016 · Cited by 22
Finding Biclusters by Random Projections
Lonardi, S., Wojciech Szpankowski, A., & Yang, Q. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27801-6_8 · Cited by 22
Penalized orthogonal-components regression for large p small n data
Zhang, D., Lin, Y., & Zhang, M. (2009) · Electronic Journal of Statistics · doi:10.1214/09-ejs354 · Cited by 22
Towards Unsupervised Gene Selection: A Matrix Factorization Framework
Li, J., & Wang, F. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2591545 · Cited by 21
Bregman Bubble Clustering: A Robust, Scalable Framework for Locating Multiple, Dense Regions in Data
Gupta, G., & Ghosh, J. (2006) · Proceedings · doi:10.1109/icdm.2006.32 · Cited by 21
Approximate Selective Inference via Maximum Likelihood
Panigrahi, S., & Taylor, J. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2081575 · Cited by 20
“Our simulation setting Following closely a simulation setup in Hastie et al. (2017), we consider a regression setting of moderate dimensions with two configurations (1) sample size n = 500 and regression size p = 100; (2) sample size n = 300 and regression size p = 100.”
BackgroundMethodKung, S., Luo, Y., & Mak, M. (2008) · Journal of Signal Processing Systems · doi:10.1007/s11265-008-0273-8 · Cited by 20
“One of the earlier methods developed was “gene shaving” [ 16 ], which sought subsets of genes with large variations across samples (or conditions) that have similar expression patterns by iteratively discarding (“shaving off”) the genes with the smallest variations.”
MethodBenetti, F., Gustincich, S., & Legname, G. (2012) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2012.659661 · Cited by 22
“By using the statistical method ‘gene shaving,’ which identifies subsets of genes with coherent expression patterns and large variation across conditions [94], a significant correlation between these two transcriptomic profiles was obtained.”
MethodDiscovering genes: the use of microarrays and laser capture microdissection in pain research
Reilly, S. C., Cossins, A. R., Quinn, J. P., & Sneddon, L. U. (2004) · Brain Research Reviews · doi:10.1016/j.brainresrev.2004.07.001 · Cited by 20
Ugolin, N., Ory, C., Lefevre, E., Benhabilès, N., Hofman, P. M., Schlumberger, M. J., & Chevillard, S. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023581 · Cited by 19
“Eigenvectors with the largest eigenvalues define the directions of the N-dimensional space that presents the greatest asymmetry (total variance) between the tumors [36].”
BackgroundUse of SVD-based probit transformation in clustering gene expression profiles
Liang, F. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.01.022 · Cited by 19
“The sine function modeling for the cell cycle behavior is supported by the experiments reported by Holter et al. (2000) and Alter et al. (2000).”
MethodCrijns, A. P. G., Gerbens, F., Plantinga, A. E. D., Meersma, G. J., de Jong, S., Hofstra, R. M. W., de Vries, E. G. E., van der Zee, A. G., de Bock, G. H., & Meerman, G. J. T. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-232 · Cited by 19
“Discussion In general FA, as effected by SVD, is applied to two-color microarray data for summarizing, filtering and preprocessing data (EFA), although several studies have shown FA can be used for gene selection [18-29].”
MethodTemporal gene expression classification with regularised neural network
Liang, Y., & Kelemen, A. (2005) · International Journal of Bioinformatics Research and Applications · doi:10.1504/ijbra.2005.008443 · Cited by 19
“…genes on the basis of gene expression profiles (Tseng et al., 2001; Baldi and Long, 2001; Tusher et al., 2001; Storey and Tibshirani, 2003; Hastiel et al., 2000; Pan, 2002; Ibrahim et al., 2002; Wolfinger et al., 2001; Eisen et al., 1998; Alter et al., 2000; Yeung and Ruzzo, 2001; Kerr…”
BackgroundChallenges and prospects in the analysis of large-scale gene expression data
Ihmels, J. (2004) · Briefings in Bioinformatics · doi:10.1093/bib/5.4.313 · Cited by 19
Finding biclusters by random projections
Lonardi, S., Wojciech Szpankowski, A., & Yang, Q. (2006) · Theoretical Computer Science · doi:10.1016/j.tcs.2006.09.023 · Cited by 19
Sheng, J., Deng, H., Calhoun, V. D., & Wang, Y. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.71 · Cited by 18
Sun, J., Herazo‐Maya, J. D., Molyneaux, P. L., Maher, T. M., Kaminski, N., & Zhao, H. (2018) · Biometrics · doi:10.1111/biom.12964 · Cited by 18
“For the gene expression dataset, it is widely recognized that most genes are redundant and selecting informative genes before clustering analysis can improve the results (Hastie et al., 2000).”
BackgroundGhelder, C. V., Esmenjaud, D., Callot, C., Dubois, E., Mazier, M., & Duval, H. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01269 · Cited by 18
Subtyping obesity with microarrays: implications for the diagnosis and treatment of obesity
Wang, S., Sparks, L. M., Xie, H., Greenway, F. L., de Jonge, L., & Smith, S. R. (2009) · International Journal of Obesity · doi:10.1038/ijo.2008.277 · Cited by 18
Lawrence, J. G. (2014) · ASM Press eBooks · doi:10.1128/9781555817640.ch2 · Cited by 18
Yu, K., Martin, R. B., & Whittemore, A. S. (2004) · Genetic Epidemiology · doi:10.1002/gepi.20016 · Cited by 18
“In this way, we obtain the nested sequence C ¼ S 0 (cid:7) S 1 (cid:7) . . . (cid:7) S M of subsets with the property that Z We then use a random permutation procedure motivated by the gap statistic of Hastie et al. [2000] to choose an ‘‘optimal’’ set S * from this sequence.”
MethodUnsupervised Feature Extraction Using Singular Value Decomposition
Modarresi, K. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.05.424 · Cited by 17
K-Boost: A Scalable Algorithm for High-Quality Clustering of Microarray Gene Expression Data
Geraci, F., Leoncini, M., Montangero, M., Pellegrini, M., & Renda, M. E. (2009) · Journal of Computational Biology · doi:10.1089/cmb.2008.0201 · Cited by 17
“, 2004), or based on principal component analysis (PCA) (Hastie et al., 2000).”
Identification of Biologically Significant Genes from Combinatorial Microarray Data
Kong, C. S., Yu, J., Minion, F. C., & Rajan, K. (2011) · ACS Combinatorial Science · doi:10.1021/co200111u · Cited by 17
Malaspina, A., & Turkheimer, F. (2006) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2006.10.032 · Cited by 17
A Robust Clustering Method and Visualization Tool Based on Data Depth
Jörnsten, R. J., Vardi, Y., & Zhang, C. (2002) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-8201-9_29 · Cited by 17
“When the sample classes are known, we can cluster genes in a supervised fashion ([4],[6],[11]).”
BackgroundA General Framework for Estimation and Inference From Clusters of Features
Reid, S., Taylor, J., & Tibshirani, R. J. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1246368 · Cited by 15
Self-citationA Dirichlet process mixture model for clustering longitudinal gene expression data
Sun, J., Herazo‐Maya, J. D., Kaminski, N., Zhao, H., & Warren, J. L. (2017) · Statistics in Medicine · doi:10.1002/sim.7374 · Cited by 16
“As commonly carried out, the genes with the largest variances are selected for clustering, because they explain a large proportion of the variance in the data, which is likely related to the underlying clustering structure in the data [43].”
BackgroundA method for detecting and correcting feature misidentification on expression microarrays
Tu, I., Schaner, M. E., Diehn, M., Sikic, B. I., Brown, P. O., Botstein, D., & Fero, M. J. (2004) · BMC Genomics · doi:10.1186/1471-2164-5-64 · Cited by 16
“Many methods have had specific implementations written for the analysis of gene expression data, such as various forms of clustering, self ordered maps, singular value decomposition and significance analysis [8-13].”
MethodSelf-citationTimmons, J. A., & Sundberg, C. J. (2006) · IUBMB Life · doi:10.1080/15216540500507390 · Cited by 17
“For example, ‘gene shaving’ ‘principal component analysis’ (PCA) and more conventional (but less robust) clustering methods can be applied ( 29, 58 – 60 ).”
MethodRossbroich, J., Durieux, J., & Wilderjans, T. F. (2022) · Journal of Classification · doi:10.1007/s00357-021-09409-1 · Cited by 20
“…protein networks where proteins are part of various protein complexes simultaneously (Palla et al., 2008), or genetic networks, in which genes influence multiple cellular functions and as such belong to several metabolic pathways (Segal et al., 2003; Battle et al., 2004; Hastie et al., 2000).”
BackgroundTroyanskaya, O. G., Botstein, D., & Altman, R. B. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/0-306-47815-3_3 · Cited by 15
Self-citationKnowledge Discovery and Emergent Complexity in Bioinformatics
Tuyls, K., Westra, R. L., Saeys, Y., & Nowé, A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-71037-0 · Cited by 15
A spectral clustering method for microarray data
Tritchler, D., Fallah, S., & Beyene, J. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.04.010 · Cited by 15
“Hastie et al. (2000) base their gene shaving method on the largest eigenvalue of a matrix closely related to the one we use.”
MethodAnalysis of bilateral inverse symmetry in whole bacterial chromosomes
Sánchez, J., & José, M. V. (2002) · Biochemical and Biophysical Research Communications · doi:10.1016/s0006-291x(02)02583-4 · Cited by 15
Underexpression of Specific Interferon Genes Is Associated with Poor Prognosis of Melanoma
Zainulabadeen, A., Yao, P., & Zare, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170025 · Cited by 15
Systematic tuning of parameters in support vector clustering
Yılmaz, Ö., Achenie, L. E. K., & Srivastava, R. (2006) · Mathematical Biosciences · doi:10.1016/j.mbs.2006.09.013 · Cited by 15
Bredel, M., Bredel, C., & Sikic, B. I. (2004) · The Lancet Oncology · doi:10.1016/s1470-2045(04)01382-8 · Cited by 15
Dyson, G. E., Frikke‐Schmidt, R., Nordestgaard, B. G., Tybjærg‐Hansen, A., & Sing, C. F. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20225 · Cited by 14
“[2004] used the peeling and pasting procedures from PRIM as a part of the gene shaving method [Hastie et al., 2000] to identify haplotypes that are most likely to share a common ancestor.”
MethodModule-based breast cancer classification
Zhang, Y., Xuan, J., Clarke, R. C., & Ressom, H. W. (2013) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2013.053309 · Cited by 14
“Over the last few decades, high-throughput genomic and proteomic techniques have generated a large number of diagnostic, prognostic and predictive molecular signatures related to many diseases (Clarke et al., 2008; van de Vijver et al., 2002; Ramaswamy et al., 2001; Golub et al., 1999; Hastie et al., 2000; Ben-Dor et…”
BackgroundCano, C., García, F., López, F. J., & Blanco, A. J. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.030 · Cited by 14
pairheatmap: Comparing expression profiles of gene groups in heatmaps
Sun, X., & Li, J. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.07.010 · Cited by 14
“…addition, some gene may be involved in several related biological groups while the classical algorithm assigned one gene to only one group To allow gene clusters to overlap, Hastie proposed a gene shaving methods to search high variation across the samples and high correlation across the genes [6 .”
A novel ranked k -nearest neighbors algorithm for missing data imputation
Khan, Y., Shah, S. F., & Asim, S. M. (2024) · Journal of Applied Statistics · doi:10.1080/02664763.2024.2414357 · Cited by 13
Optimization of gene set annotations via entropy minimization over variable clusters (EMVC)
Frost, H. R., & Moore, J. H. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu110 · Cited by 12
“, 2012), gene shaving (Hastie et al., 2000), the varclus method in the R Hmisc package, an R implementation of the SAS VARCLUS procedure, etc.”
MethodBrown, A. A., Ding, Z., Viñuela, A., Glass, D., Parts, L., Spector, T. D., Winn, J. M., & Durbin, R. M. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.011411 · Cited by 12
“Tim Spector is an NIHR Senior Investigator and is holder of an ERC Advanced Principal Investigator award.”
BackgroundInfluentialAdaptive elastic net-penalized quantile regression for variable selection
Yan, A., & Song, F. (2019) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2018.1508711 · Cited by 13
“In the high-dimensional setting, the “ grouped variables ” situation is particularly important, which has been addressed many times in the literature, see, for example, Hastie et al. (2000), Zou and Hastie (2005) and references therein.”
BackgroundBose, S., Das, C., Banerjee, A., Ghosh, K., Chattopadhyay, M., Chattopadhyay, S., & Barik, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.671 · Cited by 13
A Comprehensive Review on Computer Vision and Fuzzy Logic in Forensic Science Application
Thakkar, P., Patel, D., Hirpara, I., Jagani, J., Patel, S. K., Shah, M., & Kshirsagar, A. (2022) · Annals of Data Science · doi:10.1007/s40745-022-00408-6 · Cited by 13
“[72] Contrary to the conventional techniques, this technique firstly extracts DNA from the samples, and then the DNA replication is done.”
MethodUnsupervised adaptive floating search feature selection based on Contribution Entropy
Devakumari, D., & Thangavel, K. (2010) · 2010 International Conference on Communication and Computational Intelligence (INCOCCI) · Cited by 13
Statistical Analysis of Microarray Time Course Data
Tai, Y. C., & Speed, T. P. (2005) · Cited by 13
TZANIS, G. L., Berberidis, C., & Vlahavas, I. P. (2005) · IGI Global eBooks · doi:10.4018/978-1-59140-560-3.ch007 · Cited by 13
An exploratory data analysis method to reveal modular latent structures in high-throughput data
Yu, T. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-440 · Cited by 12
“Methods for the identification of tight clusters, such as gene-shaving [15], bi-clustering [16] and contextdependent clustering [17], cannot identify hidden factors that act in linear combinations.”
BackgroundMethodDynamic agglomerative clustering of gene expression profiles
Liang, F., & Wang, N. (2007) · Pattern Recognition Letters · doi:10.1016/j.patrec.2007.01.009 · Cited by 12
Pattern Discovery in Expression Profiling Data
Katagiri, F., & Glazebrook, J. (2009) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2205s85 · Cited by 12
“Supplement 85 Current Protocols in Molecular Biology methods that try to address such “fuzzy” clustering assignment, such as those described in Hastie et al. (2000), Gasch and Eisen (2002), and Dembele and Kastner (2003).”
MethodPaul, S., & Maji, P. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-266 · Cited by 12
“In this regard, the Gap function [49] is generally used to know whether the obtained B1 error is smaller than that would be expected by chance, if the distribution of the classmembership label of the sample did not depend on its feature vector.”
BackgroundGupta, G., & Ghosh, J. (2008) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1376815.1376817 · Cited by 12
“Also, they use a specific notion of “distance”, typically Euclidean or Mahalanobis distance, which further limits their applicability.”
BackgroundInfluentialAn ensemble method for gene discovery based on DNA mi-croarray data
Li, X. (2004) · Science in China Series C Life Sciences · doi:10.1360/03yc0127 · Cited by 12
Zhang, W., Edwards, A., Fan, W., Zhu, D., & Zhang, K. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-338 · Cited by 12
“Singular value decomposition (SVD) is a well-known matrix factorization method that has been widely applied in the analysis of microarray data [11-17].”
BackgroundMethodGenomics as a service: A joint computing and networking perspective
Reali, G., Femminella, M., Nunzi, E., & Valocchi, D. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.08.005 · Cited by 14
POE: Statistical Methods for Qualitative Analysis of Gene Expression
Garrett, E. S., & Parmigiani, G. (2003) · Statistics for biology and health · doi:10.1007/0-387-21679-0_16 · Cited by 12
“A close relative of our strategy is the “gene-shaving” algorithm ( Hastie et al., 2000 ).”
Effective dimensionality for principal component analysis of time series expression data
Hörnquist, M., Hertz, J., & Wahde, M. (2003) · Biosystems · doi:10.1016/s0303-2647(03)00128-x · Cited by 12
The Terminus Region of theEscherichia coliChromosome, or, All's Well That Ends Well
Louarn, J., Kuempel, P. L., & Cornet, F. (2014) · ASM Press eBooks · doi:10.1128/9781555817640.ch13 · Cited by 12
Advances in Cluster Analysis of Microarray Data
Sheng, Q., Moreau, Y., Smet, F. D., Kathleen Marchal, D., & Moor, B. D. (2005) · doi:10.1002/0470094419.ch10 · Cited by 12
“Examples are the gene shaving method [19], which searches for clusters of genes that vary as much as possible across the samples with the help of principle component analysis (PCA), and a minimum description length method [22] that identifies gene clusters responsible for classification of experimental conditions.”
MethodA Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain
Balsor, J. L., Arbabi, K., Singh, D., Kwan, R., Zaslavsky, J., Jeyanesan, E., & Murphy, K. M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.668293 · Cited by 11
The Median Problem for the Reversal Distance in Circular Bacterial Genomes
Ohlebusch, E., Abouelhoda, M. I., Hockel, K., & Stallkamp, J. (2005) · Lecture notes in computer science · doi:10.1007/11496656_11 · Cited by 11
A Novel Approach for Biclustering Gene Expression Data Using Modular Singular Value Decomposition
Aradhya, V. N. M., Masulli, F., & Stefano Rovetta, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14571-1_19 · Cited by 11
A Bayesian Integrative Approach for Multi-Platform Genomic Data: A Kidney Cancer Case Study
Chekouo, T., Stingo, F. C., Doecke, J. D., & Do, K.-A. (2016) · Biometrics · doi:10.1111/biom.12587 · Cited by 12
“To avoid multi-collinearity issues, we used the gene shaving algorithm of Hastie et al. (2000) to effectively identify subsets of biomarkers with correlated expression patterns across samples or conditions and assessed these along with individual biomarkers.”
MethodImmunosignature Analysis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)
Günther, O. P., Gardy, J. L., Stafford, P., Fluge, Ø., Mella, O., Tang, P., Miller, R. R., Parker, S. M., Johnston, S. A., & Patrick, D. M. (2018) · Molecular Neurobiology · doi:10.1007/s12035-018-1354-8 · Cited by 11
Software Tools for Glycan Profiling
Yu, C., Mayampurath, A. M., & Tang, H. (2012) · Methods in molecular biology · doi:10.1007/978-1-62703-146-2_18 · Cited by 11
“Then, similar to the gene-shaving technique [10], a glycan shaving algorithm based on principal component analysis (PCA) is used to identify the top ‘n’ (n is a user-specified number) glycan species that contribute most to the abundance variation [11].”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
