Abstract
BACKGROUND: Identification of biomarkers among thousands of genes arrayed for disease classification has been the subject of considerable research in recent years. These studies have focused on disease classification, comparing experimental groups of effected to normal patients. Related experiments can be done to identify tissue-restricted biomarkers, genes with a high level of expression in one tissue compared to other tissue types in the body.
RESULTS: In this study, cartilage was compared with ten other body tissues using a two color array experimental design. Thirty-seven probe sets were identified as cartilage biomarkers. Of these, 13 (35%) have existing annotation associated with cartilage including several well-established cartilage biomarkers. These genes comprise a useful database from which novel targets for cartilage biology research can be selected. We determined cartilage specific Z-scores based on the observed M to classify genes with Z-scores > or = 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.
CONCLUSION: Quantile regression is a promising method for the analysis of two color array experiments that compare multiple samples in the absence of biological replicates, thereby limiting quantifiable error. We used a nonparametric approach to reveal the relationship between percentiles of M and A, where M is log2(R/G) and A is 0.5 log2(RG) with R representing the gene expression level in cartilage and G representing the gene expression level in one of the other 10 tissues. Then we performed linear quantile regression to identify genes with a cartilage-restricted pattern of expression.
Document Type
Article
Publication Date
7-2-2008
Digital Object Identifier (DOI)
http://dx.doi.org/10.1186/1471-2105-9-300
Repository Citation
Huang, Liping; Zhu, Wenying; Saunders, Christopher P.; Macleod, James N.; Zhou, Mai; Stromberg, Arnold J.; and Bathke, Arne C., "A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data" (2008). Veterinary Science Faculty Publications. 17.
https://uknowledge.uky.edu/gluck_facpub/17
Notes/Citation Information
Published in BMC Bioinformatics, v. 9, 300.
© 2008 Huang et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.