BROWSE

Related Researcher

Author

Nam, Dougu
Statistical Genomics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

ITEM VIEW & DOWNLOAD

De-correlating expression in gene-set analysis

Cited 4 times inthomson ciCited 4 times inthomson ci
Title
De-correlating expression in gene-set analysis
Author
Nam, Dougu
Keywords
ENRICHMENT ANALYSIS; PROFILES; PATHWAYS
Issue Date
201009
Publisher
OXFORD UNIV PRESS
Citation
BIOINFORMATICS, v.26, no.18, pp.i511 - i516
Abstract
Motivation: Group-wise pattern analysis of genes, known as geneset 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.
URI
Go to Link
DOI
http://dx.doi.org/10.1093/bioinformatics/btq380
ISSN
1367-4803
Appears in Collections:
SLS_Journal Papers

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qr_code

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU