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DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions
Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3834838/ https://www.ncbi.nlm.nih.gov/pubmed/24049071 http://dx.doi.org/10.1093/nar/gkt834 |
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author | Klambauer, Günter Unterthiner, Thomas Hochreiter, Sepp |
author_facet | Klambauer, Günter Unterthiner, Thomas Hochreiter, Sepp |
author_sort | Klambauer, Günter |
collection | PubMed |
description | Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential expression. Evidence of differential expression is measured by the informative/noninformative (I/NI) value, which allows differentially expressed transcripts to be extracted at a desired specificity (significance level) or sensitivity (power). DEXUS performed excellently in identifying differentially expressed transcripts in data with unknown conditions. On 2400 simulated data sets, I/NI value thresholds of 0.025, 0.05 and 0.1 yielded average specificities of 92, 97 and 99% at sensitivities of 76, 61 and 38%, respectively. On real-world data sets, DEXUS was able to detect differentially expressed transcripts related to sex, species, tissue, structural variants or quantitative trait loci. The DEXUS R package is publicly available from Bioconductor and the scripts for all experiments are available at http://www.bioinf.jku.at/software/dexus/. |
format | Online Article Text |
id | pubmed-3834838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-38348382013-11-21 DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions Klambauer, Günter Unterthiner, Thomas Hochreiter, Sepp Nucleic Acids Res Methods Online Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential expression. Evidence of differential expression is measured by the informative/noninformative (I/NI) value, which allows differentially expressed transcripts to be extracted at a desired specificity (significance level) or sensitivity (power). DEXUS performed excellently in identifying differentially expressed transcripts in data with unknown conditions. On 2400 simulated data sets, I/NI value thresholds of 0.025, 0.05 and 0.1 yielded average specificities of 92, 97 and 99% at sensitivities of 76, 61 and 38%, respectively. On real-world data sets, DEXUS was able to detect differentially expressed transcripts related to sex, species, tissue, structural variants or quantitative trait loci. The DEXUS R package is publicly available from Bioconductor and the scripts for all experiments are available at http://www.bioinf.jku.at/software/dexus/. Oxford University Press 2013-11 2013-09-17 /pmc/articles/PMC3834838/ /pubmed/24049071 http://dx.doi.org/10.1093/nar/gkt834 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online Klambauer, Günter Unterthiner, Thomas Hochreiter, Sepp DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions |
title | DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions |
title_full | DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions |
title_fullStr | DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions |
title_full_unstemmed | DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions |
title_short | DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions |
title_sort | dexus: identifying differential expression in rna-seq studies with unknown conditions |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3834838/ https://www.ncbi.nlm.nih.gov/pubmed/24049071 http://dx.doi.org/10.1093/nar/gkt834 |
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