title: Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment.
abstract: In 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).
  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 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.
  
  
  CONCLUSIONS
  Hence 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.
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Oskar
authors.initials: O
authors.lastname: Bruning
authors.affiliation: Centre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
authors.forename: Wendy
authors.initials: W
authors.lastname: Rodenburg
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Paul F K
authors.initials: PF
authors.lastname: Wackers
authors.affiliation: Centre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
authors.forename: Conny
authors.initials: C
authors.lastname: van Oostrom
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Martijs J
authors.initials: MJ
authors.lastname: Jonker
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Rob J
authors.initials: RJ
authors.lastname: Dekker
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Han
authors.initials: H
authors.lastname: Rauwerda
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Wim A
authors.initials: WA
authors.lastname: Ensink
authors.affiliation: Centre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
authors.forename: Annemieke
authors.initials: A
authors.lastname: de Vries
authors.affiliation: RNA Biology & Applied Bioinformatics research group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
authors.forename: Timo M
authors.initials: TM
authors.lastname: Breit
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D004307:Dose-Response Relationship, Radiation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000528:radiation effects*; D008297:Male; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D012867:Skin / Q000528:radiation effects*; D013997:Time Factors; D014466:Ultraviolet Rays / Q000009:adverse effects