A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data.
Abstract: 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. Conclusions: 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.
Publication Date: 2008-07-02 PubMed ID: 18597687PubMed Central: PMC2474617DOI: 10.1186/1471-2105-9-300Google Scholar: Lookup
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- Journal Article
- Research Support
- N.I.H.
- Extramural
- Research Support
- Non-U.S. Gov't
- Research Support
- U.S. Gov't
- Non-P.H.S.
Summary
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The research mentions a novel approach using quantile regression to identify those biomarkers that are specific to the cartilage, using a two-color array experimental design. This approach involves comparing cartilage to ten other tissues in the body and identifying the genes with high expression in cartilage compared to others.
Quantile Regression Method & Experimental Design
- The researchers used a two-color array design in their experiment, wherein they compared cartilage with ten other body tissues. The aim was to identify genes that primarily express in cartilage.
- The quantile regression method was employed to analyze results from these two-color array experiments. Quantile regression is a type of regression analysis used in statistics. Unlike the regular regression analysis that predicts a mean, quantile regression predicts a percentile. Here, this method was used to compare multiple samples without biological replicates, which limits quantifiable error.
Cartilage-Specific Biomarkers
- During the study, 37 probe sets were identified as biomarkers specific to cartilage.
- Of these, 13 (or 35%) have existing annotation related to cartilage, which includes several well-established cartilage biomarkers.
- The researchers also calculated the Z-scores specific to cartilage based on the observed M. Genes that emerged with Z-scores >= 1.96 in all ten cartilage/tissue comparisons were classified as cartilage-specific.
- These identified cartilage-specific genes can be used as a useful database for future research on cartilage biology.
Significance & Conclusion
- The novel application of quantile regression revealed itself as a promising methodology in the identification of tissue-specific genes, especially when the experimental design involves the comparison of multiple tissue types without biological replicates.
- The identification and classification of cartilage-specific biomarkers can provide a valuable resource for research directed towards understanding cartilage biology and associated diseases.
- Moreover, this method can extend beyond cartilage biology; it can assist in studying and identifying biomarkers related to other tissues as well, thereby accelerating the discovery process in gene-centric medical research.
Cite This Article
APA
Huang L, Zhu W, Saunders CP, Macleod JN, Zhou M, Stromberg AJ, Bathke AC.
(2008).
A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data.
BMC Bioinformatics, 9, 300.
https://doi.org/10.1186/1471-2105-9-300 Publication
Researcher Affiliations
- Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu
MeSH Terms
- Animals
- Biomarkers / analysis
- Cartilage / metabolism
- Databases, Genetic
- Gene Expression / physiology
- Gene Expression Profiling / methods
- Horses / genetics
- Linear Models
- Oligonucleotide Array Sequence Analysis / methods
- Research Design
- Statistics, Nonparametric
Grant Funding
- P20 RR016481 / NCRR NIH HHS
- P20 RR16481 / NCRR NIH HHS
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Citations
This article has been cited 5 times.- Thomsen LN, Thomsen PD, Downing A, Talbot R, Berg LC. FOXO1, PXK, PYCARD and SAMD9L are differentially expressed by fibroblast-like cells in equine synovial membrane compared to joint capsule.. BMC Vet Res 2017 Apr 14;13(1):106.
- Caldwell R, Lin YX, Zhang R. Comparisons between Arabidopsis thaliana and Drosophila melanogaster in relation to Coding and Noncoding Sequence Length and Gene Expression.. Int J Genomics 2015;2015:269127.
- Briollais L, Durrieu G. Application of quantile regression to recent genetic and -omic studies.. Hum Genet 2014 Aug;133(8):951-66.
- Steele KH, O'Connor LH, Burpo N, Kohler K, Johnston JW. Characterization of a ferrous iron-responsive two-component system in nontypeable Haemophilus influenzae.. J Bacteriol 2012 Nov;194(22):6162-73.
- Ho JW, Stefani M, dos Remedios CG, Charleston MA. A model selection approach to discover age-dependent gene expression patterns using quantile regression models.. BMC Genomics 2009 Dec 3;10 Suppl 3(Suppl 3):S16.
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