Evidence map›Paper›PMID 26789003›Full record

ArticlePloS one2016

Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment.

Oskar Bruning, Wendy Rodenburg, Paul F K Wackers, Conny van Oostrom, Martijs J Jonker, Rob J Dekker, Han Rauwerda, Wim A Ensink, Annemieke de Vries, Timo M Breit

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Oskar BruningRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Wendy RodenburgCentre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Paul F K WackersRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Conny van OostromCentre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Martijs J JonkerRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Rob J DekkerRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Han RauwerdaRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Wim A EnsinkRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Annemieke de VriesCentre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Timo M BreitRNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
University of Amsterdam · NLNational Institute for Public Health and the Environment · NL

Funding

Humanized Mouse Models for the p53 R72P SNPR01ES015587 · NIEHS · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JOHNSON, DAVID G. · 2009 to 2013
$2.2M
NIEHS NIH HHS 1R01 ES015587-01A2NIEHS NIH HHS R01 ES015587
6 · The paper itself

Abstract

confounding factorsIn transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64). ANALYSIS APPROACH: Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes. ANOVA: ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors.

conclusionHence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

Indexed as

Analysis of VarianceAnimalsDose-Response Relationship, RadiationGene Expression ProfilingGene Expression RegulationMaleMiceOligonucleotide Array Sequence AnalysisSample SizeSkinTime FactorsUltraviolet Rays

Identifiers

PMID26789003
PMCPMC4720430
OpenAlexW2258189350

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.