Gene Expression Data Analysis Using a Novel Approach to Biclustering Combining Discrete and Continuous Data

被引:7
作者
Christinat, Yann [1 ]
Wachmann, Bernd [2 ]
Zhang, Lei [2 ]
机构
[1] Ecole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lab Computat Biol & Bioinformat, CH-1015 Lausanne, Switzerland
[2] Siemens Corp Res, Princeton, NJ 08540 USA
关键词
Data mining; biclustering algorithm; gene expression data; discrete data; simultaneous clustering; microarray analysis;
D O I
10.1109/TCBB.2007.70251
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Many different methods exist for pattern detection in gene expression data. In contrast to classical methods, biclustering has the ability to cluster a group of genes together with a group of conditions (replicates, set of patients, or drug compounds). However, since the problem is NP-complex, most algorithms use heuristic search functions and, therefore, might converge toward local maxima. By using the results of biclustering on discrete data as a starting point for a local search function on continuous data, our algorithm avoids the problem of heuristic initialization. Similar to Order-Preserving Submatrices (OPSM), our algorithm aims to detect biclusters whose rows and columns can be ordered such that row values are growing across the bicluster's columns and vice versa. Results have been generated on the yeast genome (Saccharomyces cerevisiae), a human cancer data set, and random data. Results on the yeast genome showed that 89 percent of the 100 biggest nonoverlapping biclusters were enriched with Gene Ontology annotations. A comparison with the methods OPSM and Iterative Signature Algorithm (ISA, a generalization of singular value decomposition) demonstrated a better efficiency when using gene and condition orders. We present results on random and real data sets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters.
引用
收藏
页码:583 / 593
页数:11
相关论文
共 26 条
[1]  
AGUILARRUIZ JS, 2005, P ACM S APPL COMP SA
[2]   Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling [J].
Alizadeh, AA ;
Eisen, MB ;
Davis, RE ;
Ma, C ;
Lossos, IS ;
Rosenwald, A ;
Boldrick, JG ;
Sabet, H ;
Tran, T ;
Yu, X ;
Powell, JI ;
Yang, LM ;
Marti, GE ;
Moore, T ;
Hudson, J ;
Lu, LS ;
Lewis, DB ;
Tibshirani, R ;
Sherlock, G ;
Chan, WC ;
Greiner, TC ;
Weisenburger, DD ;
Armitage, JO ;
Warnke, R ;
Levy, R ;
Wilson, W ;
Grever, MR ;
Byrd, JC ;
Botstein, D ;
Brown, PO ;
Staudt, LM .
NATURE, 2000, 403 (6769) :503-511
[3]  
[Anonymous], A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
[4]   BicAT: a biclustering analysis toolbox [J].
Barkow, S ;
Bleuler, S ;
Prelic, A ;
Zimmermann, P ;
Zitzler, E .
BIOINFORMATICS, 2006, 22 (10) :1282-1283
[5]  
Ben-Dor A., 2002, P 6 ANN INT C COMP B, P49, DOI DOI 10.1145/565196.565203
[6]   Iterative signature algorithm for the analysis of large-scale gene expression data [J].
Bergmann, S ;
Ihmels, J ;
Barkai, N .
PHYSICAL REVIEW E, 2003, 67 (03) :18
[7]   Characterizing gene sets with FuncAssociate [J].
Berriz, GF ;
King, OD ;
Bryant, B ;
Sander, C ;
Roth, FP .
BIOINFORMATICS, 2003, 19 (18) :2502-2504
[8]  
BRYAN K, 2005, P 18 IEEE S COMP BAS
[9]  
CHENG Y, 2000, P 8 INT C INT SYST M, P93
[10]   Learning to order things [J].
Cohen, WW ;
Schapire, RE ;
Singer, Y .
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 1999, 10 :243-270