Meta-analysis of gene expression data: a predictor-based approach
- PMID: 17463023
- DOI: 10.1093/bioinformatics/btm149
Meta-analysis of gene expression data: a predictor-based approach
Abstract
Motivation: With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately.
Results: We propose a new meta-analysis technique that aims at finding a set of classifying genes, whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray data sets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer data set, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers.
Supplementary information: Supplementary data are available at Bioinformatics online.
Similar articles
-
FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis.BMC Bioinformatics. 2006 Dec 12;7 Suppl 4(Suppl 4):S7. doi: 10.1186/1471-2105-7-S4-S7. BMC Bioinformatics. 2006. PMID: 17217525 Free PMC article.
-
Large scale data mining approach for gene-specific standardization of microarray gene expression data.Bioinformatics. 2006 Dec 1;22(23):2898-904. doi: 10.1093/bioinformatics/btl500. Epub 2006 Oct 10. Bioinformatics. 2006. PMID: 17032674
-
Independent component analysis-based penalized discriminant method for tumor classification using gene expression data.Bioinformatics. 2006 Aug 1;22(15):1855-62. doi: 10.1093/bioinformatics/btl190. Epub 2006 May 18. Bioinformatics. 2006. PMID: 16709589
-
Towards knowledge-based gene expression data mining.J Biomed Inform. 2007 Dec;40(6):787-802. doi: 10.1016/j.jbi.2007.06.005. Epub 2007 Jun 21. J Biomed Inform. 2007. PMID: 17683991 Review.
-
Bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers.Ann N Y Acad Sci. 2004 May;1020:32-40. doi: 10.1196/annals.1310.005. Ann N Y Acad Sci. 2004. PMID: 15208181 Review.
Cited by
-
Proceedings of the 2008 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.BMC Bioinformatics. 2008 Aug 12;9 Suppl 9(Suppl 9):S1. doi: 10.1186/1471-2105-9-S9-S1. BMC Bioinformatics. 2008. PMID: 18793454 Free PMC article. No abstract available.
-
MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature.Sci Rep. 2019 Jun 19;9(1):8770. doi: 10.1038/s41598-019-45165-4. Sci Rep. 2019. PMID: 31217513 Free PMC article.
-
Meta-analysis of genome-wide expression patterns associated with behavioral maturation in honey bees.BMC Genomics. 2008 Oct 24;9:503. doi: 10.1186/1471-2164-9-503. BMC Genomics. 2008. PMID: 18950506 Free PMC article.
-
Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data.Biometrics. 2009 Dec;65(4):1021-9. doi: 10.1111/j.1541-0420.2009.01200.x. Biometrics. 2009. PMID: 19302409 Free PMC article.
-
On integrating multi-experiment microarray data.Philos Trans A Math Phys Eng Sci. 2014 Apr 21;372(2016):20130136. doi: 10.1098/rsta.2013.0136. Print 2014 May 28. Philos Trans A Math Phys Eng Sci. 2014. PMID: 24751870 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
