ArticleScientific reports2017
Variation-preserving normalization unveils blind spots in gene expression profiling.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- The use of new approach methodologies (high-throughput transcriptomics) to study nanoagrochemicals: mechanisms of toxicity of a commercial copper oxychloride to soil model invertebrates (Enchytraeus crypticus).Archives of toxicology · 2026Article
- Epitranscriptomic analysis reveals clinical and molecular signatures in glioblastoma.Acta neuropathologica communications · 2025Article
- On virus and nanomaterials - Lessons learned from the innate immune system - ACE activation in the invertebrate model Enchytraeus crypticus.Journal of hazardous materials · 2022Article
- Annelid genomes: Enchytraeus crypticus, a soil model for the innate (and primed) immune system.Lab animal · 2021Article
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- Article
- Identifying conserved UV exposure genes and mechanisms.Scientific reports · 2018Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.
Identifiers
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Registered trials
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.