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Fold-change threshold screening: a robust algorithm to unmask hidden gene expression patterns in noisy aggregated transcriptome data
Institute for Environmental Research, RWTH Aachen University, Aachen, Germany.
Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
Research Institute for Ecosystem Analysis and Assessment - gaiac, Aachen, Germany.
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2015 (English)In: Environmental science and pollution research international, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 22, no 21, 16384-16392 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. Vol. 22, no 21, 16384-16392 p.
Keyword [en]
Bioinformatics, Masked effects, Danio rerio, Aggregated analysis, Ecotoxicogenomics, Robustness indicator (ROBI), Bayesian generalized linearmodel
National Category
Environmental Sciences
Research subject
Enviromental Science
Identifiers
URN: urn:nbn:se:oru:diva-46837DOI: 10.1007/s11356-015-5019-0ISI: 000363964700013PubMedID: 26178833Scopus ID: 2-s2.0-84945477897OAI: oai:DiVA.org:oru-46837DiVA: diva2:874517
Note

Funding Agencies:

German Federal Ministry of Education and Research (BMBF) 02WU1053

BMBF AZ:0315190 B

DanTox

Available from: 2015-11-27 Created: 2015-11-27 Last updated: 2017-10-17Bibliographically approved

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