title: An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era.
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.
authors: Zhenqiang
authors: Z
authors: Su
authors: Hong
authors: H
authors: Fang
authors: Huixiao
authors: H
authors: Hong
authors: Leming
authors: L
authors: Shi
authors: Wenqian
authors: W
authors: Zhang
authors: Wenwei
authors: W
authors: Zhang
authors: Yanyan
authors: Y
authors: Zhang
authors: Zirui
authors: Z
authors: Dong
authors: Lee J
authors: LJ
authors: Lancashire
authors: Marina
authors: M
authors: Bessarabova
authors: Xi
authors: X
authors: Yang
authors: Baitang
authors: B
authors: Ning
authors: Binsheng
authors: B
authors: Gong
authors: Joe
authors: J
authors: Meehan
authors: Joshua
authors: J
authors: Xu
authors: Weigong
authors: W
authors: Ge
authors: Roger
authors: R
authors: Perkins
authors: Matthias
authors: M
authors: Fischer
authors: Weida
authors: W
authors: Tong
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D012313:RNA / Q000032:analysis*; D051381:Rats; D017423:Sequence Analysis, RNA
chemical_list: D005819:Genetic Markers; D012313:RNA
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