Results 1 - 10
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739
Biclustering algorithms for biological data analysis: a survey
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
, 2004
"... Abstract—A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of ..."
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Cited by 481 (15 self)
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Abstract—A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications. Index Terms—Biclustering, simultaneous clustering, coclustering, subspace clustering, bidimensional clustering, direct clustering, block clustering, two-way clustering, two-mode clustering, two-sided clustering, microarray data analysis, biological data analysis, gene expression data. 1
Missing value estimation methods for DNA microarrays
, 2001
"... Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clu ..."
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Cited by 476 (26 self)
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Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.
Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
- STATISTICA SINICA
, 2002
"... DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. A ..."
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Cited by 433 (12 self)
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DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. Although it is not the main focus of the paper, new methods for the important pre-processing steps of image analysis and normalization are proposed. Given suitably normalized data, the biological question of differential expression is restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and responses or covariates of interest. Di erentially expressed genes are identified based on adjusted p-values for a multiple testing procedure which strongly controls the family-wise Type I error rate and takes into account the dependence structure between the gene expression levels. No specific parametric form is assumed for the distribution of the test statistics and a permutation procedure is used to estimate adjusted p-values. Several data displays are suggested for the visual identification of differentially expressed genes and of important features of these genes. The above methods are applied to microarray data from a study of gene expression in the livers of mice with very low HDL cholesterol levels. The genes identified using data from multiple slides are compared to those identified by recently published single-slide methods.
Discovering Statistically Significant Biclusters in Gene Expression Data
- In Proceedings of ISMB 2002
, 2002
"... In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under p ..."
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Cited by 299 (4 self)
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In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).
Inferring Subnetworks from Perturbed Expression Profiles
, 2001
"... Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structur ..."
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Cited by 206 (13 self)
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Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways. Contact: danab@cs.huji.ac.il
Analyzing time series gene expression data
- Bioinformatics
, 2004
"... doi:10.1093/bioinformatics/bth283 ..."
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DNA microarray-mediated transcriptional profiling of the Escherichia coli response to hydrogen peroxide
- J. Bacteriol
, 2001
"... Updated information and services can be found at: ..."
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Cited by 128 (3 self)
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Updated information and services can be found at:
Transcriptional profiling shows that Gcn4p is a master regulator of gene expression during amino acid starvation in
, 2001
"... Starvation for amino acids induces Gcn4p, a transcriptional activator of amino acid biosynthetic genes in Saccharomyces cerevisiae. In an effort to identify all genes regulated by Gcn4p during amino acid starvation, we performed cDNA microarray analysis. Data from 21 pairs of hybridization experimen ..."
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Cited by 127 (7 self)
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Starvation for amino acids induces Gcn4p, a transcriptional activator of amino acid biosynthetic genes in Saccharomyces cerevisiae. In an effort to identify all genes regulated by Gcn4p during amino acid starvation, we performed cDNA microarray analysis. Data from 21 pairs of hybridization experiments using two different strains derived from S288c revealed that more than 1,000 genes were induced, and a similar number were repressed, by a factor of 2 or more in response to histidine starvation imposed by 3-aminotriazole (3AT). Profiling of a gcn4 � strain and a constitutively induced mutant showed that Gcn4p is required for the full induction by 3AT of at least 539 genes, termed Gcn4p targets. Genes in every amino acid biosynthetic pathway except cysteine and genes encoding amino acid precursors, vitamin biosynthetic enzymes, peroxisomal components, mitochondrial carrier proteins, and autophagy proteins were all identified as Gcn4p targets. Unexpectedly, genes involved in amino acid biosynthesis represent only a quarter of the Gcn4p target genes. Gcn4p also activates genes involved in glycogen homeostasis, and mutant analysis showed that Gcn4p suppresses glycogen levels in amino acid-starved cells. Numerous genes encoding protein kinases and transcription factors were identified as targets, suggesting that Gcn4p is a master regulator of gene expression. Interestingly, expression profiles for 3AT and the alkylating agent methyl methanesulfonate (MMS) overlapped extensively, and MMS induced GCN4 translation. Thus, the broad transcriptional response evoked by Gcn4p is produced
Genome-wide discovery of transcriptional modules from DNA sequence and gene expression
- Bioinformatics
, 2003
"... In this paper, we describe an approach for understanding
transcriptional regulation from both gene expression and
promoter sequence data. We aim to identify transcriptional
modules—sets of genes that are co-regulated in a set
of experiments, through a common motif profile. Using
the EM algorithm, o ..."
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Cited by 124 (7 self)
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In this paper, we describe an approach for understanding
transcriptional regulation from both gene expression and
promoter sequence data. We aim to identify transcriptional
modules—sets of genes that are co-regulated in a set
of experiments, through a common motif profile. Using
the EM algorithm, our approach refines both the module
assignment and the motif profile so as to best explain
the expression data as a function of transcriptional motifs.
It also dynamically adds and deletes motifs, as required
to provide a genome-wide explanation of the expression
data. We evaluate the method on two Saccharomyces
cerevisiae gene expression data sets, showing that our
approach is better than a standard one at recovering
known motifs and at generating biologically coherent
modules. We also combine our results with binding
localization data to obtain regulatory relationships with
known transcription factors, and show that many of the
inferred relationships have support in the literature.
