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

Jonas Hausen1, Jens C Otte2, Uwe Strähle2

  • 1Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52074, Aachen, Germany. jhausen@bio5.rwth-aachen.de.

Environmental Science and Pollution Research International
|July 17, 2015
PubMed
Summary

This study introduces a new algorithm to identify key genes affected by environmental contaminants in transcriptomics data. The method effectively reduces noise and selects robust indicator genes for environmental monitoring.

Keywords:
Aggregated analysisBayesian generalized linear modelBioinformaticsDanio rerioEcotoxicogenomicsMasked effectsRobustness indicator (ROBI)

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Area of Science:

  • Environmental Science
  • Genomics
  • Bioinformatics

Background:

  • Transcriptomics is crucial for studying genetic responses to environmental contamination.
  • Data noise and a large number of differentially expressed genes complicate the identification of reliable contaminant indicators.
  • Robust algorithms are needed to reduce data dimensionality and select significant genes.

Purpose of the Study:

  • To develop and present an algorithm for aggregated analysis of transcriptome data.
  • To identify robust discriminative genes as potential indicators of environmental contaminants.
  • To assess gene robustness using multiple fold-change thresholds and Bayesian generalized linear models.

Main Methods:

  • An algorithm utilizing multiple fold-change thresholds (threshold screening) was developed.
  • Bayesian generalized linear models were employed to assess gene significance.
  • A robustness indicator (ROBI) and significance profiles were generated to evaluate gene reliability.

Main Results:

  • The algorithm successfully identified eight discriminative genes from a Danio rerio dataset exposed to chlorpyrifos, methylmercury, and PCB.
  • Significance profiles revealed specific genetic effects and optimal fold-change thresholds for gene clusters.
  • The developed method effectively reduced data dimensionality and selected robust indicator genes.

Conclusions:

  • The presented algorithm, incorporating fold-change threshold screening, is a powerful tool for feature selection in transcriptomics.
  • This approach effectively reduces the number of detected genes, making it suitable for environmental monitoring.
  • The method can unmask subtle genetic expression patterns obscured by noise and reduce the likelihood of Type II errors in environmental screening.