A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data
<p>Abstract</p> <p>Background</p> <p>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 grou...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2008-07-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/300 |
| _version_ | 1819180065990115328 |
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| author | Stromberg Arnold J Zhou Mai MacLeod James N Saunders Christopher P Zhu Wenying Huang Liping Bathke Arne C |
| author_facet | Stromberg Arnold J Zhou Mai MacLeod James N Saunders Christopher P Zhu Wenying Huang Liping Bathke Arne C |
| author_sort | Stromberg Arnold J |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 ≥ 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.</p> <p>Conclusion</p> <p>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 log<sub>2</sub>(R/G) and A is 0.5 log<sub>2</sub>(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.</p> |
| first_indexed | 2024-12-22T22:08:21Z |
| format | Article |
| id | doaj.art-e4d7ae23e60b49ab9be54888132a5043 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T22:08:21Z |
| publishDate | 2008-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-e4d7ae23e60b49ab9be54888132a50432022-12-21T18:10:55ZengBMCBMC Bioinformatics1471-21052008-07-019130010.1186/1471-2105-9-300A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray dataStromberg Arnold JZhou MaiMacLeod James NSaunders Christopher PZhu WenyingHuang LipingBathke Arne C<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 ≥ 1.96 in all ten cartilage/tissue comparisons as cartilage-specific genes.</p> <p>Conclusion</p> <p>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 log<sub>2</sub>(R/G) and A is 0.5 log<sub>2</sub>(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.</p>http://www.biomedcentral.com/1471-2105/9/300 |
| spellingShingle | Stromberg Arnold J Zhou Mai MacLeod James N Saunders Christopher P Zhu Wenying Huang Liping Bathke Arne C A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data BMC Bioinformatics |
| title | A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data |
| title_full | A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data |
| title_fullStr | A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data |
| title_full_unstemmed | A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data |
| title_short | A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data |
| title_sort | novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data |
| url | http://www.biomedcentral.com/1471-2105/9/300 |
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