Universität zu Köln

Su, Zhenqiang, Fang, Hong, Hong, Huixiao, Shi, Leming ORCID: 0000-0002-7143-2033, Zhang, Wenqian, Zhang, Wenwei, Zhang, Yanyan, Dong, Zirui, Lancashire, Lee J., Bessarabova, Marina, Yang, Xi ORCID: 0000-0002-3427-0565, Ning, Baitang, Gong, Binsheng ORCID: 0000-0002-8724-5435, Meehan, Joe, Xu, Joshua, Ge, Weigong, Perkins, Roger, Fischer, Matthias and Tong, Weida (2014). An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol., 15 (12). LONDON: BMC. ISSN 1474-760X

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Abstract

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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Su, Zhenqiang UNSPECIFIED UNSPECIFIED UNSPECIFIED Fang, Hong UNSPECIFIED UNSPECIFIED UNSPECIFIED Hong, Huixiao UNSPECIFIED UNSPECIFIED UNSPECIFIED Shi, Leming UNSPECIFIED orcid.org/0000-0002-7143-2033 UNSPECIFIED Zhang, Wenqian UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Wenwei UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Yanyan UNSPECIFIED UNSPECIFIED UNSPECIFIED Dong, Zirui UNSPECIFIED UNSPECIFIED UNSPECIFIED Lancashire, Lee J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Bessarabova, Marina UNSPECIFIED UNSPECIFIED UNSPECIFIED Yang, Xi UNSPECIFIED orcid.org/0000-0002-3427-0565 UNSPECIFIED Ning, Baitang UNSPECIFIED UNSPECIFIED UNSPECIFIED Gong, Binsheng UNSPECIFIED orcid.org/0000-0002-8724-5435 UNSPECIFIED Meehan, Joe UNSPECIFIED UNSPECIFIED UNSPECIFIED Xu, Joshua UNSPECIFIED UNSPECIFIED UNSPECIFIED Ge, Weigong UNSPECIFIED UNSPECIFIED UNSPECIFIED Perkins, Roger UNSPECIFIED UNSPECIFIED UNSPECIFIED Fischer, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED Tong, Weida UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-452193
DOI:	10.1186/s13059-014-0523-y
Journal or Publication Title:	Genome Biol.
Volume:	15
Number:	12
Date:	2014
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1474-760X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language GENE-EXPRESSION SIGNATURE; REPRODUCIBILITY Multiple languages Biotechnology & Applied Microbiology; Genetics & Heredity Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/45219