Results 1  10
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202
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 Kmeans clu ..."
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Cited by 477 (24 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 Kmeans 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.
Fast Monte Carlo Algorithms for Matrices II: Computing a LowRank Approximation to a Matrix
 SIAM JOURNAL ON COMPUTING
, 2004
"... ... matrix A. It is often of interest to find a lowrank approximation to A, i.e., an approximation D to the matrix A of rank not greater than a specified rank k, where k is much smaller than m and n. Methods such as the Singular Value Decomposition (SVD) may be used to find an approximation to A ..."
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Cited by 216 (20 self)
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... matrix A. It is often of interest to find a lowrank approximation to A, i.e., an approximation D to the matrix A of rank not greater than a specified rank k, where k is much smaller than m and n. Methods such as the Singular Value Decomposition (SVD) may be used to find an approximation to A which is the best in a well defined sense. These methods require memory and time which are superlinear in m and n; for many applications in which the data sets are very large this is prohibitive. Two simple and intuitive algorithms are presented which, when given an m n matrix A, compute a description of a lowrank approximation D to A, and which are qualitatively faster than the SVD. Both algorithms have provable bounds for the error matrix A D . For any matrix X , let kXk and kXk 2 denote its Frobenius norm and its spectral norm, respectively. In the rst algorithm, c = O(1) columns of A are randomly chosen. If the m c matrix C consists of those c columns of A (after appropriate rescaling) then it is shown that from C C approximations to the top singular values and corresponding singular vectors may be computed. From the computed singular vectors a description D of the matrix A may be computed such that rank(D ) k and such that holds with high probability for both = 2; F . This algorithm may be implemented without storing the matrix A in Random Access Memory (RAM), provided it can make two passes over the matrix stored in external memory and use O(m + n) additional RAM memory. The second algorithm is similar except that it further approximates the matrix C by randomly sampling r = O(1) rows of C to form a r c matrix W . Thus, it has additional error, but it can be implemented in three passes over the matrix using only constant ...
Aligning Gene Expression Time Series With Time Warping Algorithms
, 2001
"... Motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression ..."
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Cited by 150 (3 self)
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Motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression states in different time series to be mapped to one another. Results: We present implementations of time warping algorithms applicable to RNA and protein expression data and demonstrate their application to published yeast RNA expression time series. Programs executing two warping algorithms are described, a simple warping algorithm and an interpolative algorithm, along with programs that generate graphics that visually present alignment information. We show time warping to be superior to simple clustering at mapping corresponding time states. We document the impact of statistical measurement noise and sample size on the quality of time alignments, and present issues related to statistical assessment of alignment quality through alignment scores. We also discuss directions for algorithm improvement including development of multiple time series alignments and possible applications to causality searches and nontemporal processes (`concentration warping'). Availability: Academic implementations of alignment programs genewarp and genewarpi and the graphics generation programs grphwarp and grphwarpi are available as Win32 system DOS box executables on our web site along with documentation on their use. The publicly available data on which they were demonstrated may be found at http://genomewww.stanford.edu/cellcycle/. Postscript files generated by grphwarp and grphwarpi may be directly printed or viewed using GhostView software available at http://www.cs.wisc.edu/#ghost/. Con...
Cluster Analysis for Gene Expression Data: A Survey
 IEEE Transactions on Knowledge and Data Engineering
, 2004
"... Abstract—DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity f ..."
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Cited by 149 (5 self)
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Abstract—DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field. Index Terms—Microarray technology, gene expression data, clustering.
From patterns to pathways: gene expression data analysis comes of age.
 Nature Genetics
, 2002
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Spectral biclustering of microarray data: coclustering genes and conditions.
 In Genome Research,
, 2003
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A Bayesian missing value estimation method for gene expression profile data
 Bioinformatics
, 2003
"... Motivation: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the t ..."
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Cited by 127 (2 self)
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Motivation: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes
Analysis of Gene Expression Data with Pathway Scores
, 2000
"... We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for different problem specifications. The significance of the scores ..."
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Cited by 59 (2 self)
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We present a new approach for the evaluation of gene expression data. The basic idea is to generate biologically possible pathways and to score them with respect to gene expression measurements. We suggest sample scoring functions for different problem specifications. The significance of the scores for the investigated pathways is assessed by comparison to a number of scores for random pathways. We show that simple scoring functions can assign statistically significant scores to biologically relevant pathways. This suggests that the combination of appropriate scoring functions with the systematic generation of pathways can be used in order to select the most interesting pathways based on gene expression measurements.
An empirical study of Principal Component Analysis for clustering gene expression data
, 2001
"... Motivation: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expressio ..."
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Cited by 57 (4 self)
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Motivation: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PC's) in capturing cluster structure. In other words, we empirically compared the quality of clusters obtained from the original data set to the quality of clusters obtained from clustering the PC's using both real and synthetic gene expression data sets. Results: Our empirical study showed that clustering with the PC's instead of the original variables does not necessarily improve, and often degrade, cluster quality. In particular, the first few PC's (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PC's has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances. Availability: The software is under development. Contact: kayee cs.washington.edu Supplementary information: http://www.cs.washington.edu/homes/kayee/pca 1
Detecting stable clusters using principal component analysis
 In Functional Genomics: Methods and Protocols. M.J. Brownstein and A. Kohodursky (eds.) Humana press, 2003
"... Clustering is one of the most commonly used tools in the analysis of gene expression data (1, 2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is thus a preliminary step in extracting gene networks and inference of gene function (3, 4). Clus ..."
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Cited by 39 (1 self)
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Clustering is one of the most commonly used tools in the analysis of gene expression data (1, 2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is thus a preliminary step in extracting gene networks and inference of gene function (3, 4). Clustering of experiments can be used to discover novel