The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

Charles Wang, Binsheng Gong, Pierre R. Bushel, Jean Thierry-Mieg, Danielle Thierry-Mieg, Joshua Xu, Hong Fang, Huixiao Hong, Jie Shen, Zhenqiang Su, Joe Meehan, Xiaojin Li, Lu Yang, Haiqing Li, Pawel P. Labaj, David P. Kreil, Dalila Megherbi, Stan Gaj, Florian Caiment, Joost van DelftJos Kleinjans, Andreas Scherer, Viswanath Devanarayan, Jian Wang, Yong Yang, Hui-Rong Qian, Lee J. Lancashire, Marina Bessarabova, Yuri Nikolsky, Cesare Furlanello, Marco Chierici, Davide Albanese, Giuseppe Jurman, Samantha Riccadonna, Michele Filosi, Roberto Visintainer, Ke K. Zhang, Jainying Li, Jui-Hua Hsieh, Daniel L. Svoboda, James C. Fuscoe, Youping Deng, Leming Shi, Richard S. Paules, Scott S. Auerbach, Weida Tong*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R-2 approximate to 0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.
Original languageEnglish
Pages (from-to)926-932
JournalNature Biotechnology
Volume32
Issue number9
DOIs
Publication statusPublished - Sept 2014

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