Biclustering of gene expression data by correlation-based scatter search.
BACKGROUND
The analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed. Most of them use the Mean Squared Residue as merit function but interesting and relevant patterns from a biological point of view such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of patterns since commonly the genes can present a similar behavior although their expression levels vary in different ranges or magnitudes.


METHODS
Scatter Search is an evolutionary technique that is based on the evolution of a small set of solutions which are chosen according to quality and diversity criteria. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method is included in order to select just positively correlated genes.


RESULTS
The proposed algorithm has been tested with three real data sets such as Yeast Cell Cycle dataset, human B-cells lymphoma dataset and Yeast Stress dataset, finding a remarkable number of biclusters with shifting and scaling patterns. In addition, the performance of the proposed method and fitness function are compared to that of CC, OPSM, ISA, BiMax, xMotifs and Samba using Gene the Ontology Database.
Dpt, Lenguajes y Sistemas Informáticos, ETSII, University of Seville, Avd, Reina Mercedes s/n, 41012, Seville, Spain. janepo@us.es.
Juan A
JA
Nepomuceno
Alicia
A
Troncoso
Jesús S
JS
Aguilar-Ruiz
Bioinformatics. 2006 May 1;22(9):1122-9
16500941
Nature. 2000 Feb 3;403(6769):503-11
10676951
Algorithms Mol Biol. 2009 Jun 04;4:8
19497096
BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S27
19208127
IEEE/ACM Trans Comput Biol Bioinform. 2004 Jan-Mar;1(1):24-45
17048406
BMC Bioinformatics. 2009 Apr 29;10 Suppl 4:S9
19426457
Mol Biol Cell. 2000 Dec;11(12):4241-57
11102521
Mol Cell. 1998 Jul;2(1):65-73
9702192
Nat Genet. 1999 Jan;21(1 Suppl):33-7
9915498
Genome Res. 2003 Apr;13(4):703-16
12671006
Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103
10977070
BMC Res Notes. 2009 Jul 07;2:124
19583847
Bioinformatics. 2004 Sep 1;20(13):1993-2003
15044247
Proc Natl Acad Sci U S A. 2000 Oct 24;97(22):12079-84
11035779
Pac Symp Biocomput. 2003;:77-88
12603019
Bioinformatics. 2009 Nov 1;25(21):2795-801
19734153
Bioinformatics. 2005 Oct 15;21(20):3840-5
16144809
IEEE Symp Comput Intell Data Min. 2007 Mar 1;2007:624-629
20953276
BioData Min. 2009 Dec 16;2:9
20015398
BMC Bioinformatics. 2008 Apr 23;9:209
18433477
J Comput Biol. 2003;10(3-4):373-84
12935334
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902
12689096
IEEE/ACM Trans Comput Biol Bioinform. 2010 Jan-Mar;7(1):153-65
20150677
Brief Bioinform. 2006 Mar;7(1):86-112
16761367
Bioinformatics. 2002;18 Suppl 1:S136-44
12169541