ArticleNature biotechnology2014
The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance.
Article in Nature biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 300 papers, 2 of them syntheses that pooled it.
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Who cites it
300 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- The Role of Microarray in Modern Sequencing: Statistical Approach Matters in a Comparison Between Microarray and RNA-Seq.Biotech (Basel (Switzerland)) · 2025Article
- Identification and correction of time-series transcriptomic anomalies.Nucleic acids research · 2025Article
- Identification of Prognostic Biomarkers in Gene Expression Profile of Neuroblastoma Via Machine Learning.Pediatric discovery · 2025Article
- Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis.Journal of the American Statistical Association · 2025Article
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- Serum RNA Profile Reflects Fluid Status and Atrophic Retinal Changes in Neovascular Age-Related Macular Degeneration.International journal of molecular sciences · 2025Article
240 more citing papers are in PubMed but not listed here.
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Authors and funding
46 authors.
Funding
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)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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.