caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.
BACKGROUND
The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables.


RESULTS
In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenotype clustering (wherein phenotype labels for samples are known), albeit with minor algorithm modifications customized to each of these tasks.


CONCLUSIONS
VISDA achieved robust and superior clustering accuracy, compared with several benchmark clustering schemes. The model order selection scheme in VISDA was shown to be effective for high dimensional genomic data clustering. On muscular dystrophy data and muscle regeneration data, VISDA identified biologically relevant co-expressed gene clusters. VISDA also captured the pathological relationships among different phenotypes revealed at the molecular level, through phenotype clustering on muscular dystrophy data and multi-category cancer data.
Zhu
Yitan
Y
Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu
Li
Huai
H
Miller
David J
DJ
Wang
Zuyi
Z
Xuan
Jianhua
J
Clarke
Robert
R
Hoffman
Eric P
EP
Wang
Yue
Y
D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D003196:Computer Graphics; D003198:Computer Simulation; D030541:Databases, Genetic; D008957:Models, Genetic; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
BMC Bioinformatics. 2007;8:3
17204155
Bioinformatics. 2001;17 Suppl 1:S306-15
11473022
Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8
9843981
J Comput Biol. 1999 Fall-Winter;6(3-4):281-97
10582567
IEEE Trans Neural Netw. 2000;11(3):625-36
18249790
Brain. 2006 Apr;129(Pt 4):996-1013
16478798
Bioinformatics. 2006 Apr 1;22(7):795-801
16434443
Proc Natl Acad Sci U S A. 2001 Dec 18;98(26):15149-54
11742071
Genome Inform. 2004;15(1):239-48
15712126
Bioinformatics. 2001 Jul;17(7):658-9
11448886
IEEE Trans Neural Netw. 2005 May;16(3):645-78
15940994
Science. 2007 Feb 16;315(5814):972-6
17218491
Bioinformatics. 2003 May 22;19(8):973-80
12761060
Nat Genet. 1999 Jul;22(3):281-5
10391217
IEEE Trans Biomed Eng. 2004 May;51(5):707-18
15132496
Mol Cell. 2002 Mar;9(3):587-600
11931766
Neural Comput. 2004 Jun;16(6):1299-323
15130251
Front Biosci. 2008;13:677-90
17981579
Bioinformatics. 2001 Oct;17(10):977-87
11673243
Am J Pathol. 2003 Nov;163(5):1985-95
14578198
Bioinformatics. 2002 Nov;18(11):1462-9
12424117
BMC Bioinformatics. 2008;9 Suppl 2:S4
18387206
Bioinformatics. 2004 Jan 1;20(1):85-92
14693813
Gene Regul Syst Bio. 2008 Jan 15;1:349-63
19936101
Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262-7
10618406
Br J Cancer. 2008 Mar 25;98(6):1023-8
18283324
Nat Rev Cancer. 2008 Jan;8(1):37-49
18097463
Bioinformatics. 2007 Aug 1;23(15):2024-7
17540678
BMC Bioinformatics. 2007;8:250
17626636
Conf Proc IEEE Eng Med Biol Soc. 2006;1:5767-70
17946331
BMC Bioinformatics. 2003 Sep 6;4:36
12959646
Am J Pathol. 2001 Oct;159(4):1231-8
11583950
EURASIP J Bioinform Syst Biol. 2007;:64628
17713593
Neural Comput. 1999 Feb 15;11(2):443-82
9950739
BMC Bioinformatics. 2006;7:397
16945146
BMC Bioinformatics. 2007;8 Suppl 2:S7
17493256
C R Biol. 2003 Oct-Nov;326(10-11):1049-65
14744113
Bioinformatics. 2004 Aug 12;20(12):1905-13
15044244
Front Biosci. 2008;13:3839-49
18508478
Proc Natl Acad Sci U S A. 1999 Mar 16;96(6):2907-12
10077610
Science. 1999 Oct 15;286(5439):531-7
10521349