Discovering statistically significant biclusters in gene expression data

Tanay, Amos; Sharan, Roded; Shamir, Ron

doi:10.1093/bioinformatics/18.suppl_1.s136

Cited by 767 publications

(589 citation statements)

References 17 publications

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“…Moreover, it allows overlap among modules, which is essential when analyzing systems with multiple-function genes. Extant analysis techniques (8,9,17,(19)(20)(21) lack one or more of these characteristics. SAMBA is built to exploit the emerging repositories of very large-scale functional genomics data and is highly efficient and scalable in both memory and speed.…”

Section: Resultsmentioning

confidence: 99%

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data

Tanay

Sharan

Kupiec

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

348

263

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The dissection of complex biological systems is a challenging task, made difficult by the size of the underlying molecular network and the heterogeneous nature of the control mechanisms involved. Novel high-throughput techniques are generating massive data sets on various aspects of such systems. Here, we perform analysis of a highly diverse collection of genomewide data sets, including gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. By integrating experimental data of heterogeneous sources and types, we are able to perform analysis on a much broader scope than previous studies. At the core of our methodology is the ability to identify modules, namely, groups of genes with statistically significant correlated behavior across diverse data sources. Numerous biological processes are revealed through these modules, which also obey global hierarchical organization. We use the identified modules to study the yeast transcriptional network and predict the function of >800 uncharacterized genes. Our analysis framework, SAMBA (Statistical-Algorithmic Method for Bicluster Analysis), enables the processing of current and future sources of biological information and is readily extendable to experimental techniques and higher organisms.

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Section: Resultsmentioning

confidence: 99%

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data

Tanay

Sharan

Kupiec

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

348

263

View full text Add to dashboard Cite

show abstract

“…The thresholding BMF algorithm is used in our experiments since the datasets are generally sparse. We compare it with four other methods, BiMax [23], ISA [11,10], SAMBA [26],and Binary Non-Orthogonal Matrix Decomposition [19](BND for short). Note that BND is based on heuristics while BMF is based on non-linear programming.…”

Section: Results Analysismentioning

confidence: 99%

Binary Matrix Factorization with Applications

Zhang

Ding

et al. 2007

Seventh IEEE International Conference on Data Mining (ICDM 2007)

138

127

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“…-Exhaustive bicluster enumeration: enumerating all possible biclusters to identify the best ones in exponential time [10]. -Distribution parameter identification: assuming the data is generated from a model and trying to fit parameters of that model by minimizing a certain criterion [7].…”

Section: Related Workmentioning

confidence: 99%

A Repeated Local Search Algorithm for BiClustering of Gene Expression Data

Truong

Battiti

Brunato

2013

Similarity-Based Pattern Recognition

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Abstract. Given a gene expression data matrix where each cell is the expression level of a gene under a certain condition, biclustering is the problem of searching for a subset of genes that coregulate and coexpress only under a subset of conditions. The traditional clustering algorithms cannot be applied for biclustering as one cannot measure the similarity between genes (or rows) and conditions (or columns) by normal geometric similarities. Identifying a network of collaborating genes and a subset of experimental conditions which activate the specific network is a crucial part of the problem. In this paper, the BIClustering problem is solved through a REpeated Local Search algorithm, called BICRELS. The experiments on real datasets show that our algorithm is not only fast but it also significantly outperforms other state-of-the-art algorithms.

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Discovering statistically significant biclusters in gene expression data

Cited by 767 publications

References 17 publications

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data

Binary Matrix Factorization with Applications

A Repeated Local Search Algorithm for BiClustering of Gene Expression Data

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