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 ApproachEvaluation 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.ANOVAANOVA 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.

Highlights

  • By definition, Analysis Of Variance (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

  • Our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments

  • Over the years these techniques matured by enhancing the detection levels, reducing the technical noise, improving the bioinformatics analyses, and so on, resulting in greatly improved detectors compared to previous techniques

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Summary

Introduction

The arrival of microarray technology for genomics and transcriptomics plus similar genome-wide techniques for proteomics and metabolomics led to a number of major developments in experiment design, laboratory execution, and data analysis [1,2,3,4,5,6,7,8,9,10,11,12] Over the years these techniques matured by enhancing the detection levels, reducing the technical noise, improving the bioinformatics analyses, and so on, resulting in greatly improved detectors compared to previous techniques. Microarray technology and next-generation sequencing for instance, have proven their potential in biomarker applications and genome-wide screening approaches, regular transcriptome studies into unravelling gene-expression pathways and networks, often result in limited new insights. Knowledge and understanding of confounding factors will at least put transcriptomics results in a clearer biological perspective, or better yet, force the experimenter to take a hard look at the design for transcriptomics experimentation

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