Exploiting sample variability to enhance multivariate analysis of microarray data
Affiliation
Paterson Institute for Cancer Research, Cancer Research UK, Manchester, M20 4BX, UK. cmoller@picr.man.ac.ukIssue Date
2007-10-15
Metadata
Show full item recordAbstract
MOTIVATION: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses. RESULTS: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data. AVAILABILITY: Matlab script files are available from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Citation
Exploiting sample variability to enhance multivariate analysis of microarray data. 2007, 23 (20):2733-40 BioinformaticsJournal
BioinformaticsDOI
10.1093/bioinformatics/btm441PubMed ID
17827205Type
ArticleLanguage
enISSN
1460-2059ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btm441