ArticleMicroarrays (Basel, Switzerland)2014
Variation of RNA Quality and Quantity Are Major Sources of Batch Effects in Microarray Expression Data.
Article in Microarrays (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Experimental reporting of fish transcriptomic responses in environmental toxicology and ecotoxicology.Environmental toxicology and chemistry · 2025Review
- Adapter dimer contamination in sRNA-sequencing datasets predicts sequencing failure and batch effects and hampers extracellular vesicle-sRNA analysis.Journal of extracellular biology · 2023Article
- Optimization of RNA extraction methods from human metabolic tissue samples of the COMET biobank.Scientific reports · 2021Article
- Sequence characteristics define trade-offs between on-target and genome-wide off-target hybridization of oligoprobes.PloS one · 2018Article
- Bias and misleading concepts in an Arnica research study. Comments to improve experimental Homeopathy.Journal of Ayurveda and integrative medicineReview
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
The great utility of microarrays for genome-scale expression analysis is challenged by the widespread presence of batch effects, which bias expression measurements in particular within large data sets. These unwanted technical artifacts can obscure biological variation and thus significantly reduce the reliability of the analysis results. It is largely unknown which are the predominant technical sources leading to batch effects. We here quantitatively assess the prevalence and impact of several known technical effects on microarray expression results. Particularly, we focus on important factors such as RNA degradation, RNA quantity, and sequence biases including multiple guanine effects. We find that the common variation of RNA quality and RNA quantity can not only yield low-quality expression results, but that both factors also correlate with batch effects and biological characteristics of the samples.
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