ZFIN ID: ZDB-PUB-150717-8
Fold-change threshold screening: a robust algorithm to unmask hidden gene expression patterns in noisy aggregated transcriptome data
Hausen, J., Otte, J.C., Strähle, U., Hammers-Wirtz, M., Hollert, H., Keiter, S.H., Ottermanns, R.
Date: 2015
Source: Environmental science and pollution research international   22(21): 16384-92 (Journal)
Registered Authors: Otte, Jens, Strähle, Uwe
Keywords: Bioinformatics, Masked effects, Danio rerio, Aggregated analysis, Ecotoxicogenomics, Robustness indicator (ROBI), Bayesian generalized linear model
MeSH Terms:
  • Algorithms*
  • Animals
  • Bayes Theorem
  • Gene Expression Profiling/methods*
  • Statistics as Topic/methods*
  • Zebrafish/genetics
PubMed: 26178833 Full text @ Environ. Sci. Pollut. Res. Int.
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.