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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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“The procedure bears a lot of similarity with the unsupervised “gene shaving” method [15], which uses eigenvalues in lieu of weights to perform the elimination procedure.”
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BackgroundJupiter, D. C., Chen, H., & VanBuren, V. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-332 · Cited by 67
“For example, clustering methods [4,25-27] offer a simple way to group genes into modules of (potentially) interacting and interrelated genes.”
MethodLandgrebe, J., Welzl, G., Metz, T., van Gaalen, M. M., Ropers, H., Wurst, W., & Holsboer, F. (2002) · Journal of Psychiatric Research · doi:10.1016/s0022-3956(01)00061-9 · Cited by 67
Breast cancer prognosis by combinatorial analysis of gene expression data
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“Numerous statistical and machine learning methods have been successfully applied to the analysis of microarray datasets; these methods include cluster analysis (hierarchical clustering [4-7], self-organizing maps [8-10], and two-way clustering [11]), regression analysis [12], nearest neighborhood methods [14], decisio…”
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“Clustering is widely used to find these coregulated genes[5-8].”
BackgroundBiclustering Models for Structured Microarray Data
Turner, H., Bailey, T., Krzanowski, W. J., & Hemingway, C. A. (2005) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2005.49 · Cited by 64
“to supervised gene shaving [8], optimize the layer model”
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“Space constraints prevent an adequate exposition of significance testing and the reader is referred to [21,45,46,47,48,49,50,51].”
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“Hastie et al. (2000) proposed the “gene shaving” method, a statistical method to identify distinct clusters of genes.”
Methods of Determining the Number of Clusters in a Data Set and a New Clustering Criterion
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“(41); and the 60 cancer cell line (NCI 60) data set of Ross et al.”
BackgroundDisease-specific genomic analysis: identifying the signature of pathologic biology
Nicolau, M. M., Tibshirani, R. J., Børresen‐Dale, A., & Jeffrey, S. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm033 · Cited by 61
“…includes genes that vary a lot among distinct tumors (Alon et al., 1999; Dudoit Fridlyand et al., 2002; Eisen et al., 1998; Golub et al., 1999; Hastie et al., 2000; Weinstein et al., 1997), genes whose expression is stable among different samples from the same patient (Sørlie et al., 2003;…”
BackgroundSelf-citationHoude, A. L. S., Akbarzadeh, A., Günther, O. P., Li, S., Patterson, D. A., Farrell, A. P., Hinch, S. G., & Miller, K. M. (2019) · Journal of Experimental Biology · doi:10.1242/jeb.198036 · Cited by 61
DNA microarrays for expression profiling
Hughes, T. R., & Shoemaker, D. D. (2001) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(00)00163-0 · Cited by 61
Shaving diffusion tensor images in discriminant analysis: A study into schizophrenia
Caan, M. W. A., Vermeer, K. A., A, L. J. V. V., Majoie, C. B. L. M., Peters, B. D., den Heeten, G. J., & Vos, F. M. (2006) · Medical Image Analysis · doi:10.1016/j.media.2006.07.006 · Cited by 60
ClustVarLV: An R Package for the Clustering of Variables Around Latent Variables
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“It has usually been addressed using hierarchical clustering algorithms based on similarity indices between each pair of genes defined by their linear correlation coefficient, the absolute value or the squared value of the linear correlation coefficient (see, among others, Eisen et al. 1998; Hastie et al. 2000; Park et…”
MethodDrăghici, S., Kulaeva, O. I., Hoff, B., Petrov, A., Shams, S., & Tainsky, M. A. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg165 · Cited by 59
“Other related techniques include using conditional density error models [27], mixed models [41], a limit fold change model [28], gene shaving techniques [14] and many others.”
MethodFreyhult, E., Landfors, M., Önskog, J., Hvidsten, T. R., & Rydén, P. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-503 · Cited by 59
“Below follows a list of gene selection methods that are adopted in this study; STD Select the N genes that have the highest standard deviation [27].”
MethodUnsupervised feature selection using weighted principal components
Kim, S. B., & Rattakorn, P. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.10.063 · Cited by 58
“Hastie et al. (2000) developed a gene-shaving method that used its first principal component to identify the best subsets of those features with large variations.”
MethodZhao, H., Liew, A. W., Xie, X., & Yan, H. (2007) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2007.11.030 · Cited by 58
Identifying splits with clear separation: a new class discovery method for gene expression data
von Heydebreck, A., Huber, W. K., Poustka, A., & Vingron, M. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s107 · Cited by 58
“In this respect, ISIS is related to some other recent approaches to microarray data analysis (Califano et al., 2000; Chen & Church, 2000; Hastie et al., 2000, 2001; Getz et al., 2000), which also investigate relations between subsets of genes and samples.”
MethodRelating Amino Acid Sequence to Phenotype: Analysis of Peptide‐Binding Data
Segal, M. R., Cummings, M. P., & Hubbard, A. (2001) · Biometrics · doi:10.1111/j.0006-341x.2001.00632.x · Cited by 58
Chang, J., Zhou, W., Zhou, W.-X., & Wang, L. (2016) · Biometrics · doi:10.1111/biom.12552 · Cited by 57
A non-parametric approach to population structure inference using multilocus genotypes
Liu, N., & Zhao, H. (2006) · Human Genomics · doi:10.1186/1479-7364-2-6-353 · Cited by 56
“noted that finer structure can be lost with any dimension reduction method.(49) Ando proposed an algorithm which differs from SVD in that terms and documents are treated in a non-symmetrical way.”
BackgroundAdditive Risk Models for Survival Data with High‐Dimensional Covariates
Ma, S., Kosorok, M. R., & Fine, J. P. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00405.x · Cited by 56
“Another well-developed clustering-based algorithm is the gene harvesting procedure of Hastie et al. (2001).”
BackgroundMethodData Analysis and Visualization in Genomics and Proteomics
Azuaje, F. J., & Dopazo, J. (2005) · Cited by 56
E-CAST: a data mining algorithm for gene expression data
Bellaachia, A., Portnoy, D., Chen, Y., & ELKAHLOUN, A. G. (2002) · Cited by 55
Simon, R. M. (2003) · ACM SIGKDD Explorations Newsletter · doi:10.1145/980972.980978 · Cited by 54
“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 [10].”
MethodClustering Random Curves Under Spatial Interdependence With Application to Service Accessibility
Jiang, H., & Serban, N. (2012) · Technometrics · doi:10.1080/00401706.2012.657106 · Cited by 53
“The literature for functional data clustering is divided into hard clustering methods (Hastie et al. 2000; Bar-Joseph et al. 2002; Serban 2008, 2009) and model-based clustering (Fraley and Raftery 2002; Wakefield, Zhou, and Self 2002; James and Sugar 2003).”
MethodSanga, S., Broom, B. M., Vittorio Cristini, C., & Edgerton, M. E. (2009) · BMC Medical Genomics · doi:10.1186/1755-8794-2-59 · Cited by 53
“Network Inference Analysis of "Molecular Apocrine" Phenotype Using Gene Shaving & Robust Bayesian Network Analysis First, we perform an unsupervised gene clustering using Gene Shaving (GS), and subsequently use Robust Bayesian Network Analysis (RBNA) to discover relationships between an AR-based cluster and other gene…”
InfluentialCava, C., Bertoli, G. R., & Castiglioni, I. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0211-x · Cited by 57
“Techniques such as Singular value decomposition (SVD) or Principal Component Analysis (PCA) are the most popular ones for reducing the dimension of gene data [289, 290].”
MethodToxicogenomics: a new revolution in drug safety
Castle, A. L., Carver, M. P., & Mendrick, D. L. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02327-9 · Cited by 54
Selecting dissimilar genes for multi-class classification, an application in cancer subtyping
Cai, Z., Goebel, R. G., Salavatipour, M. R., & Lin, G. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-206 · Cited by 52
“Other approaches that follow the same rule, with certain modification and refinement, include t-test [4], regularized t-test [6], supervised gene shaving [2], a method focusing on expression homogeneity in a certain class [7], a method which uses both small within-class expression variance and large between-class expr…”
BackgroundMethodForoushani, A. B. K., Agrahari, R., Docking, R., Chang, L., Duns, G., Hudoba, M., Karsan, A., & Zare, H. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0253-6 · Cited by 50
“In contrast, because an eigengene is a weighted average expression of several genes, our systematic and holistic approach is much more robust than the alternative approaches that select one or a few genes from each module [72, 73].”
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Fontes, M., & Soneson, C. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-307 · Cited by 50
“The gap statistic is defined as in [25,26].”
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Maji, P. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.210 · Cited by 51
“Ç”
MethodAnalysis and Management of Microarray Gene Expression Data
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Tree-structured supervised learning and the genetics of hypertension
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Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models
Berger, J. A., Hautaniemi, S., Mitra, S. K., & Astola, J. T. (2006) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2006.10 · Cited by 50
DNA microarrays: raising the profile
van Berkum, N. L., & Holstege, F. C. P. (2001) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(00)00173-7 · Cited by 50
Spotting the target: microarrays for disease gene discovery
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Remondini, D., O’Connell, B. C., Intrator, N., Sedivy, J. M., Neretti, N., Castellani, G., & Cooper, L. N. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502081102 · Cited by 50
Functional bioinformatics of microarray data: from expression to regulation
Moreau, Y., Smet, F. D., Thijs, G., Kathleen Marchal, D., & Moor, B. D. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.804681 · Cited by 50
“, the self-organizing tree algorithm [SOTA] [18], quality-based clustering [21], adaptive quality-based clustering [10], model-based clustering [15], [58], simulated annealing [35], gene shaving [17], the cluster affinity search technique [CAST] [5]).”
BackgroundMethodOpen source libraries and frameworks for biological data visualisation: A guide for developers
Wang, R., Pérez‐Riverol, Y., Hermjakob, H., & Vizcaíno, J. A. (2014) · PROTEOMICS · doi:10.1002/pmic.201400377 · Cited by 47
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BackgroundYe, G., Chen, Y., & Xie, X. S. (2011) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 48
Simultaneous Unsupervised Learning of Disparate Clusterings
Jain, P., Meka, R., & Dhillon, I. S. (2008) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10007 · Cited by 47
Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data
Wang, Z., & Wang, C. Y. (2010) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1550 · Cited by 49
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Guerra–Urzola, R., Deun, K. V., Vera, J. C., & Sijtsma, K. (2021) · Psychometrika · doi:10.1007/s11336-021-09773-2 · Cited by 57
“However, because of the several possible choices for the rotation matrix Q , non-unique solutions in problems (2) and (4) are achieved (Hastie et al. 2000).”
BackgroundAzotobacter Genomes: The Genome of Azotobacter chroococcum NCIMB 8003 (ATCC 4412)
Robson, R. L., Jones, R., Robson, R. M., Schwartz, A., & Richardson, T. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127997 · Cited by 45
Large-scale gene expression analysis in molecular target discovery
Orr, M., & Scherf, U. (2002) · Leukemia · doi:10.1038/sj.leu.2402413 · Cited by 45
Multivariate exploratory tools for microarray data analysis
Szabó, A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.4.555 · Cited by 43
“The idea of discarding ‘interesting’ genes obtained at each step of a selection procedure and then repeating the selection procedure to find additional genes is somewhat similar to the approach by Hastie et al. (2000). To formulate a stopping rule, one needs to determine the properties of an optimal set of genes in a…”
ResultExpression Profiling of Human Tumors: The End of Surgical Pathology?
Ladanyi, M., Chan, W. C., Triche, T. J., & Gerald, W. L. (2001) · Journal of Molecular Diagnostics · doi:10.1016/s1525-1578(10)60657-5 · Cited by 43
Self-citationMantel statistics to correlate gene expression levels from microarrays with clinical covariates
Shannon, W. D., Watson, M. A., Perry, A., & Rich, K. M. (2002) · Genetic Epidemiology · doi:10.1002/gepi.1115 · Cited by 42
Determination of tumour marker genes from gene expression data
Čuperlović‐Culf, M., Belacel, N., & Ouellette, R. J. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(05)03393-3 · Cited by 42
Yazdani, A., Yazdani, A., Samiei, A., & Boerwinkle, E. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.01.012 · Cited by 40
“In genetics and epidemiology, application of PCA for summarizing genome information is frequent [e.g. 26–29].”
Additive risk survival model with microarray data
Ma, S., & Huang, J. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-192 · Cited by 41
“Another well developed clustering based algorithm is the gene harvesting procedure of [6].”
Single-layer artificial neural networks for gene expression analysis
Narayanan, A., Keedwell, E. C., Gamalielsson, J., & Tatineni, S. (2004) · Neurocomputing · doi:10.1016/j.neucom.2003.10.017 · Cited by 41
Input Selection Methods for Soft Sensor Design: A Survey
Curreri, F., Fiumara, G., & Xibilia, M. G. (2020) · Future Internet · doi:10.3390/fi12060097 · Cited by 40
“The second one generalizes linear PCA into the nonlinear case using the kernel method: the original input vectors are mapped into a higher dimensional feature space in which the linear PCA is then calculated.”
MethodInfluentialMining meiosis and gametogenesis with DNA microarrays
Schlecht, U., & Primig, M. (2003) · Reproduction · doi:10.1530/rep.0.1250447 · Cited by 40
“Normalized or scaled data are analysed with filtering algorithms to identify differentially regulated transcripts (Hastie et al., 2000, 2001; Lockhart and Winzeler, 2000).”
MethodMay, C., Nordhoff, E., Casjens, S., Turewicz, M., Eisenacher, M., Gold, R., Brüning, T., Pesch, B., Stephan, C., Woitalla, D., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089596 · Cited by 40
“The set of proteins resulting from this univariate preselection was narrowed down by multivariate selection using a random forest (RF) classifier wrapped with a backward elimination approach (‘‘gene shaving’’ (GS), [17,24,25]).”
MethodRelevant and Significant Supervised Gene Clusters for Microarray Cancer Classification
Maji, P., & Das, C. (2012) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2012.2193590 · Cited by 39
Gupta, G., Liu, A. E., & Ghosh, J. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2008.32 · Cited by 39
Model-based methods to identify multiple cluster structures in a data set
Galimberti, G., & Soffritti, G. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.019 · Cited by 42
McLachlan, G. J., & Khan, R. N. (2004) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2004.02.002 · Cited by 39
“The analysis of gene expression microarray data using clustering techniques has an important role to play in the discovery, validation, and understanding of various classes and subclasses of cancer; see, for example, [2,4,6,7,9,15,17,20,28,30,40,43,46] among several others.”
Blind source separation methods for deconvolution of complex signals in cancer biology
Zinovyev, A. Y., Kairov, U., Karpenyuk, T. A., & Ramanculov, E. (2012) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2012.12.043 · Cited by 39
“One particular approach for feature selection in matrix factorization is connected to the “gene shaving” idea [32], which consists in iterative removal of the top-scored genes and re-adjusting the components to the remaining data.”
MethodPeterson, L. E. (2003) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(02)00009-3 · Cited by 38
“Hierarchical cluster analysis links together the closest nodes containing two or more data points agglomerated during the clustering, until all points are linked together [28–30].”
BackgroundMultilevel Functional Clustering Analysis
Serban, N., & Jiang, H. (2012) · Biometrics · doi:10.1111/j.1541-0420.2011.01714.x · Cited by 38
“Specific applications include gene expression profiling from microarray studies (Hastie et al., 2000; Bar-Joseph et al., 2002; Wakefield, Zhou, and Self, 2003; Serban and Wasserman, 2005; Booth, Casella, and Hobert, 2008), clustering subjects by their spinal bone mineral density (James and Sugar,…”
BackgroundMethodPossibilistic approach for biclustering microarray data
Cano, C., Adarve, L., López, J. M., & Blanco, A. J. (2007) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2007.01.005 · Cited by 38
Logical analysis of diffuse large B-cell lymphomas
Alexe, G., Alexe, S., Axelrod, D. E., Hammer, P. L., & Weissmann, D. J. (2005) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.11.004 · Cited by 38
“[20] used a technique called ‘‘gene shaving’’, based on principle component analysis.”
MethodComplete genome sequence of the phenanthrene-degrading soil bacterium Delftia acidovorans Cs1-4
Shetty, A., de Gannes, V., Obi, C. C., Lucas, S. M., Lapidus, A. L., Cheng, J., Goodwin, L., Pitluck, S., Peters, L. M., Mikhailova, N., et al. (2015) · Standards in Genomic Sciences · doi:10.1186/s40793-015-0041-x · Cited by 37
Fuzzy association rules for biological data analysis: A case study on yeast
Lopez, F. J., Blanco, A. J., Garcia, F., Cano, C., & Marin, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-107 · Cited by 37
“[6].”
BackgroundMethodInfluentialPrincipal component gene set enrichment (PCGSE)
Frost, H. R., Li, Z., & Moore, J. H. (2015) · BioData Mining · doi:10.1186/s13040-015-0059-z · Cited by 33
Genomic Clinical Trials and Predictive Medicine
Simon, R. M. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139026451 · Cited by 35
Lee, J. K., Williams, P. D., & Cheon, S. (2008) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2007.10.010 · Cited by 36
“The gene shaving approach has been suggested to find the clusters directly relevant to major variance directions of an array data set [3].”
BackgroundUsing bioinformatics and genome analysis for new therapeutic interventions
Mount, D. W., & Pandey, R. (2005) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-05-0150 · Cited by 35
Transcriptional profiling in cancer: the path to clinical pharmacogenomics
Slonim, D. K. (2001) · Pharmacogenomics · doi:10.1517/14622416.2.2.123 · Cited by 35
SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery
Hu‐Lieskovan, S., Bhaumik, S., Dhodapkar, K. M., Grivel, J., Gupta, S. V., Hanks, B. A., Janetzki, S., Kleen, T. O., Koguchi, Y., Lund, A. W., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-000705 · Cited by 35
How do components of evidence-based psychological treatment cluster in practice?
Gifford, E. V., Tavakoli, S., Weingardt, K. R., Finney, J. W., Pierson, H. M., Rosen, C. S., Hagedorn, H., Cook, J. M., & Curran, G. M. (2011) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2011.07.008 · Cited by 34
“Cluster analysis has been used to identify patient typologies (McDermott & Weiss, 1995; Rielage, Hoyt, & Renshaw, 2010; Walker, Baber, Garber, & Smith, 2008), sets of genes with similar expression patterns (Hastie et al., 2000), and groups that may benefit from specific interventions (Beitchman et al.”
MethodFinding Groups in Gene Expression Data
Hand, D. J., & Heard, N. A. (2005) · BioMed Research International · doi:10.1155/jbb.2005.215 · Cited by 34
“Methods for identifying such groups which start with a set of genes and sequentially remove blocks of the genes until some criterion is optimised have been termed by Hastie et al [11] as “gene shaving.”
BackgroundMacDonald, T. J., Pollack, I. F., Okada, H., Bhattacharya, S., & Lyons‐Weiler, J. (2007) · Methods in molecular biology · doi:10.1007/978-1-59745-390-5_13 · Cited by 34
Molecular profiling of breast cancer: portraits but not physiognomy.
Brenton, J. D., Aparicio, S. A. J. R., & Caldas, C. (2001) · Breast Cancer Research · doi:10.1186/bcr274 · Cited by 34
“A minimal set of genes can then be chosen [7] for validation on tissue microarrays [8].”
BackgroundAbraham, Y., Zhang, X., & Parker, C. N. (2014) · SLAS DISCOVERY · doi:10.1177/1087057114524987 · Cited by 32
A multi-stage approach to clustering and imputation of gene expression profiles
Wong, D. S. V., Wong, F., & Wood, G. R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm053 · Cited by 33
“Based on the Rand and adjusted Rand indices, the clustering method is more consistent with the biological classification of the data than commonly used clustering methods.”
MethodAnalysis of complex methylation data
Siegmund, K. D., & Laird, P. W. (2002) · Methods · doi:10.1016/s1046-2023(02)00071-3 · Cited by 33
Using DNA microarrays for diagnostic and prognostic prediction
Simon, R. M. (2003) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.3.5.587 · Cited by 33
“The method of gene shaving attempts to provide linear combinations with properties similar to the principal components and does not require measuring all of the genes [26] .”
MethodKüffner, R., Fundel, K., & Zimmer, R. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1143 · Cited by 33
“Further processing takes place on the 960 000 abstracts that reference proteins from this set.”
BackgroundPredicting postoperative language outcome using presurgical fMRI, MEG, TMS, and high gamma ECoG
Abbas Babajani-feremi, A., Holder, C. M., Narayana, S., Fulton, S. P., Choudhri, A. F., Boop, F. A., & Wheless, J. W. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.12.031 · Cited by 32
Role of gene expression microarray analysis in finding complex disease genes
Gu, C. C., Rao, D. C., Stormo, G. D., Hicks, C., & Province, M. A. (2002) · Genetic Epidemiology · doi:10.1002/gepi.220 · Cited by 32
“Recent developments in this direction include the multiple latent class analysis [Lazzeroni and Owen, 2000] and a modified principal component analysis [Hastie et al., 2000].”
MethodGlobal gene expression reveals stress-responsive genes in Aspergillus fumigatus mycelia
Takahashi, H., Kusuya, Y., Hagiwara, D., Takahashi-Nakaguchi, A., Sakai, K., & Gonoi, T. (2017) · BMC Genomics · doi:10.1186/s12864-017-4316-z · Cited by 32
“applied to estimate the cluster size [43].”
MethodLiang, Y., & Kelemen, A. (2005) · Functional & Integrative Genomics · doi:10.1007/s10142-005-0006-z · Cited by 31
“It differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in the temporal fashion in that the genes may belong to more than one cluster and the clustering may be supervised by an outcome measure, such as survival (Hastiel et al. 2000).”
Redestig, H., Weicht, D., Selbig, J., & Hannah, M. A. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-454 · Cited by 31
“The Gap statistic has previously been proposed as a method for simultaneously choosing a suitable cluster size and assessing its statistical quality [31].”
MethodClustering multivariate spatial data based on local measures of spatial autocorrelationPreprint
Scrucca, L. (2005) · RePEc: Research Papers in Economics · Cited by 31
Regulation probability method for gene selection
Wang, H., & Huang, D.-S. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.07.007 · Cited by 30
“, 2000) and various wrapper methods (Weston et al., 2000; Hastie et al., 2000; Pochet et al., 2004), etc.”
BackgroundA Visual Data Mining Tool that Facilitates Reconstruction of Transcription Regulatory Networks
Jupiter, D. C., & VanBuren, V. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001717 · Cited by 30
Ollesch, J., Theegarten, D., Altmayer, M., Darwiche, K., Hager, T., Stamatis, G., & Gerwert, K. B. (2016) · Biomedical Spectroscopy and Imaging · doi:10.3233/bsi-160144 · Cited by 32
“Briefly, random forests were used to determine the Gini-importance of single spectral features for correct classification [8,10,17,18].”
MethodAccounting for Non-genetic Factors Improves the Power of eQTL Studies
Stegle, O., Kannan, A., Durbin, R. M., & Winn, J. M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-78839-3_35 · Cited by 29
“Such models have been used to represent causes of variation such as cellular function (7), regulation of gene expression (8), co-expression of genes (9) or environmental conditions (10).”
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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