De-correlating expression in gene-set analysis.
BACKGROUND
Group-wise pattern analysis of genes, known as gene-set analysis (GSA), addresses the differential expression pattern of biologically pre-defined gene sets. GSA exhibits high statistical power and has revealed many novel biological processes associated with specific phenotypes. In most cases, however, GSA relies on the invalid assumption that the members of each gene set are sampled independently, which increases false predictions.


RESULTS
We propose an algorithm, termed DECO, to remove (or alleviate) the bias caused by the correlation of the expression data in GSAs. This is accomplished through the eigenvalue-decomposition of covariance matrixes and a series of linear transformations of data. In particular, moderate de-correlation methods that truncate or re-scale eigenvalues were proposed for a more reliable analysis. Tests of simulated and real experimental data show that DECO effectively corrects the correlation structure of gene expression and improves the prediction accuracy (specificity and sensitivity) for both gene- and sample-randomizing GSA methods.


BACKGROUND
The MATLAB codes and the tested data sets are available at ftp://deco.nims.re.kr/pub or from the author.
Nam
Dougu
D
School of Nano-Biotechnology and Chemical Engineering, Ulsan National Institute of Science and Technology, Republic of Korea. dougnam@unist.ac.kr
D000465:Algorithms; D003198:Computer Simulation; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D016158:Genes, p53; D006801:Humans; D008297:Male; D011467:Prostate / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000235:genetics
BMC Cancer. 2007 Apr 12;7:64
17430594
BMC Bioinformatics. 2005 Jun 08;6:144
15941488
Ann Appl Stat. 2008 Sep 1;2(3):986-1012
19756232
Bioinformatics. 2005 Sep 15;21(18):3587-95
15994189
Proc Natl Acad Sci U S A. 2000 Aug 29;97(18):10101-6
10963673
Nat Genet. 2003 Jul;34(3):267-73
12808457
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Bioinformatics. 2007 Apr 15;23(8):980-7
17303618
BMC Bioinformatics. 2009 Feb 03;10:47
19192285
J Clin Oncol. 2004 Jul 15;22(14):2790-9
15254046
Proc Natl Acad Sci U S A. 2005 Sep 20;102(38):13544-9
16174746
Stat Appl Genet Mol Biol. 2005;4:Article32
16646851
Bioinformatics. 2001 Sep;17(9):763-74
11590094
Brief Bioinform. 2009 Jan;10(1):24-34
18836208
Brief Bioinform. 2008 May;9(3):189-97
18202032
Bioinformatics. 2007 Feb 15;23(4):401-7
17182697
Pac Symp Biocomput. 2000;:455-66
10902193