An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
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- 作者：Zhenqiang Su (1) (2)
Hong Fang (1)
Huixiao Hong (1)
Leming Shi (3) (4) (5)
Wenqian Zhang (1)
Wenwei Zhang (6)
Yanyan Zhang (6)
Zirui Dong (6) (7)
Lee J Lancashire (2)
Marina Bessarabova (2)
Xi Yang (1)
Baitang Ning (1)
Binsheng Gong (1)
Joe Meehan (1)
Joshua Xu (1)
Weigong Ge (1)
Roger Perkins (1)
Matthias Fischer (8)
Weida Tong (1)
1. National Center for Toxicological Research ; US Food and Drug Administration ; 3900 NCTR Road ; Jefferson ; AR ; 72079 ; USA
2. Thomson Reuters ; IP & Science ; 22 Thomson Place ; Boston ; MA ; 02210 ; USA
3. State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology ; Schools of Life Sciences and Pharmacy ; Fudan University ; Shanghai ; 201203 ; China
4. Fudan-Zhangjiang Center for Clinical Genomics ; Shanghai ; 201203 ; China
5. Zhanjiang Center for Translational Medicine ; Shanghai ; 201203 ; China
6. BGI-Shenzhen ; Main Building ; Bei Shan Industrial Zone ; Yantian District ; Shenzhen ; Guangdong ; 518083 ; China
7. BGI-Guangzhou ; Guangzhou ; China
8. Department of Pediatric Oncology and Hematology and Center for Molecular Medicine (CMMC) ; University Children鈥檚 Hospital of Cologne ; Kerpener Strasse 62 ; D-50924 ; Cologne ; Germany - 刊名：Genome Biology
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：15
- 期：12
- 全文大小：2,206 KB
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- 出版者：BioMed Central
- ISSN：1465-6906
文摘
Background Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? Results We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Conclusions Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.