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Fold-change threshold screening: a robust algorithm to unmask hidden gene expression patterns in noisy aggregated transcriptome data

Environmental Science and Pollution Research volume 22, pages 16384–16392 (2015)Cite this article

Abstract

Transcriptomics is often used to investigate changes in an organism’s genetic response to environmental contamination. Data noise can mask the effects of contaminants making it difficult to detect responding genes. Because the number of genes which are found differentially expressed in transcriptome data is often very large, algorithms are needed to reduce the number down to a few robust discriminative genes. We present an algorithm for aggregated analysis of transcriptome data which uses multiple fold-change thresholds (threshold screening) and p values from Bayesian generalized linear model in order to assess the robustness of a gene as a potential indicator for the treatments tested. The algorithm provides a robustness indicator (ROBI) as well as a significance profile, which can be used to assess the statistical significance of a given gene for different fold-change thresholds. Using ROBI, eight discriminative genes were identified from an exemplary dataset (Danio rerio FET treated with chlorpyrifos, methylmercury, and PCB) which could be potential indicators for a given substance. Significance profiles uncovered genetic effects and revealed appropriate fold-change thresholds for single genes or gene clusters. Fold-change threshold screening is a powerful tool for dimensionality reduction and feature selection in transcriptome data, as it effectively reduces the number of detected genes suitable for environmental monitoring. In addition, it is able to unmask patterns in altered genetic expression hidden by data noise and reduces the chance of type II errors, e.g., in environmental screening.

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Acknowledgments

The present study was part of the research funding project DanTox (DanTox—a novel joint research project using zebrafish (Danio rerio) to identify specific toxicity and molecular modes of action of sediment-bound pollutants). The authors acknowledge the financial support by the German Federal Ministry of Education and Research (BMBF grant 02WU1053). Also, the authors acknowledge the data provision from the GENDarT2 project (BMBF grant AZ:0315190 B).

Conflict of interest

The authors declare that they have no competing interests.

Author information

Affiliations

  1. Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany

    Jonas Hausen, Henner Hollert, Steffen H. Keiter & Richard Ottermanns

  2. Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany

    Jens C. Otte & Uwe Strähle

  3. Research Institute for Ecosystem Analysis and Assessment - gaiac, Kackertstraße 10, 52072, Aachen, Germany

    Monika Hammers-Wirtz

  4. Man-Technology-Environment Research Centre, Örebro University, 701 82, Örebro, Sweden

    Steffen H. Keiter

Authors
  1. Jonas Hausen
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  2. Jens C. Otte
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  3. Uwe Strähle
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  4. Monika Hammers-Wirtz
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  5. Henner Hollert
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  6. Steffen H. Keiter
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  7. Richard Ottermanns
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Correspondence to Jonas Hausen.

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Responsible editor: Philippe Garrigues

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Hausen, J., Otte, J.C., Strähle, U. et al. Fold-change threshold screening: a robust algorithm to unmask hidden gene expression patterns in noisy aggregated transcriptome data. Environ Sci Pollut Res 22, 16384–16392 (2015). https://doi.org/10.1007/s11356-015-5019-0

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  • DOI: https://doi.org/10.1007/s11356-015-5019-0

Keywords

  • Bioinformatics
  • Masked effects
  • Danio rerio
  • Aggregated analysis
  • Ecotoxicogenomics
  • Robustness indicator (ROBI)
  • Bayesian generalized linear model