ArticleNucleic acids research2015
A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- Meta-analysis of RNA interaction profiles of RNA-binding protein using the RBPInper tool.Bioinformatics advances · 2024Article
- ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles.Patterns (New York, N.Y.) · 2021Article
- The functional false discovery rate with applications to genomics.Biostatistics (Oxford, England) · 2021Article
- Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis.Frontiers in genetics · 2021Article
- Identification of lncRNA biomarkers for lung cancer through integrative cross-platform data analyses.Aging · 2020Article
- Aging induces aberrant state transition kinetics in murine muscle stem cells.Development (Cambridge, England) · 2020Article
- Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging.Genome research · 2019Article
- Understanding the Bacterial Response to Mycotoxins: The Transcriptomic Analysis of Deoxynivalenol-Induced Changes inFrontiers in pharmacology · 2019Article
- Population genomics of parallel evolution in gene expression and gene sequence during ecological adaptation.Scientific reports · 2018Article
- A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Article
- Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.Journal of the National Cancer Institute · 2017Article
- Systems-level analysis of mechanisms regulating yeast metabolic flux.Science (New York, N.Y.) · 2016Article
- Prognostic significance of PLIN1 expression in human breast cancer.Oncotarget · 2016Article
- Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease.PLoS genetics · 2016Article
- Comparative Transcriptomic Approaches Exploring Contamination Stress Tolerance in Salix sp. Reveal the Importance for a Metaorganismal de Novo Assembly Approach for Nonmodel Plants.Plant physiology · 2016Article
- A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets.Bioinformation · 2016Article
- GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.PloS one · 2015Article
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3 authors.
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Abstract
Understanding the differences between microarray and RNA-Seq technologies for measuring gene expression is necessary for informed design of experiments and choice of data analysis methods. Previous comparisons have come to sometimes contradictory conclusions, which we suggest result from a lack of attention to the intensity-dependent nature of variation generated by the technologies. To examine this trend, we carried out a parallel nested experiment performed simultaneously on the two technologies that systematically split variation into four stages (treatment, biological variation, library preparation and chip/lane noise), allowing a separation and comparison of the sources of variation in a well-controlled cellular system, Saccharomyces cerevisiae. With this novel dataset, we demonstrate that power and accuracy are more dependent on per-gene read depth in RNA-Seq than they are on fluorescence intensity in microarrays. However, we carried out quantitative PCR validations which indicate that microarrays may demonstrate greater systematic bias in low-intensity genes than in RNA-seq.
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