Coupled two-way clustering analysis of gene microarray data.

By Gad Getz, Erel Levine, Eytan Domany. Proceedings of the National Academy of Sciences of the United States of America, 2000

899 citations recorded by the source corpus.

We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c67100828f90934ee279751a375bed1b0a401d86

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Citations in the corpus

Discovering statistically significant biclusters in gene expression data (2002, methodology): “Getz et al. (2000) devised a coupled twoway iterative clusteringalgorithm to identify biclusters. Lazzeroni and Owen (2000) introduced the notion of a plaid model, which describes the input matrix as a linearfunctionof variablescorrespondingto its biclusters. They showed how to estimatea model using”
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data (2009, background): “(7) presented a coupled two-way clustering approach that employs hierarchical clustering to each separate dimension, and then combines the clustering results along each dimension in a somewhat problem-speciﬁc manner.”
Finding large average submatrices in high dimensional data (2009, neutral): “G N ] 8 O ct 2 00 9 The Annals of Applied Statistics 2009, Vol. 3, No. 3, 985–1012 DOI: 10.1214/09-AOAS239 c© Institute of Mathematical Statistics, 2009”
Iterative signature algorithm for the analysis of large-scale gene expression data. (2002, background): “In particular, a well-recognized drawback of commonly used clustering algorithms is the fact that they assign each gene to a single cluster, while in fact genes that participate in several functions should be included in multiple clusters [19, 20, 21, 22].”
Revealing modular organization in the yeast transcriptional network (2002, background): “But although standard clustering methods classify genes successfully when applied to relatively small data sets, their use in the analysis of large-scale expression data is limited by two well-recognized drawback”
Cluster analysis for gene expression data: a survey (2004, background): “Recently, a series of subspace clustering methods have been proposed [22], [11], [40] to capture coherence exhibited by the “blocks” within gene expression matrices.”
Variable Selection for Model-Based Clustering (2006, methodology): “In the non–model-based clustering context, work on variable selection has been done by Dy and Brodley (2000), Lazzeroni and Owen (2002), Getz, Levine, and Domany (2000), McLachlan, Bean, and Peel (2002), McCallum, Nigam, and Ungar (2000), Brusco and Cradit (2001), Devaney and Ram (1997), Friedman…”
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method (2001, methodology): “A widely used technique * To whom correspondence should be addressed. for microarray data analysis is clustering analysis (Alon et al., 1999; Ben-Dor et al., 1999; Bittner et al., 1999; Getz et al., 2000a,b; Hartuv et al., 2000).”
FABIA: factor analysis for bicluster acquisition (2010, background): “Received on December 23, 2009; revised on March 26, 2010; accepted on April 20, 2010”
A Stability Based Method for Discovering Structure in Clustered Data (2001, background): “Tricarboxylic acid cycle or Krebs cycle (14 genes), (2) Respiration chain complexes (27 genes), (3) Cytoplasmaticribosomal proteins (121 genes), (4) proteasomes (35 genes), and (5) Histones (11 genes).”
Biclustering algorithms for biological data analysis: a survey (2004, positive): “Any reasonable choice of clustering method and definition of stable cluster can be used within the framework of CTWC. Getz et al. used a hierarchical clustering algorithm, whose input is a similarity matrix between the rows computed according to the column set, and vice versa.”
Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification (2005, methodology): “SPC is suitable for the clustering of gene expression profiles due to its stability against noise and the inherent measure of cluster stability (Getz et al., 2000).”
Determination of network of residues that regulate allostery in protein families using sequence analysis (2006, methodology): “To this end, we used the coupled two-way clustering (CTWC) that was developed to analyze DNA-microarray data (Getz et al. 2000).”