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Fischer, D.S. ; Theis, F.J. ; Yosef, N.*

Impulse model-based differential expression analysis of time course sequencing data.

Nucleic Acids Res. 46:e119 (2018)
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Open Access Gold
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Temporal changes to the concentration of molecular species such as mRNA, which take place in response to various environmental cues, can often be modeled as simple continuous functions such as a single pulse (impulse) model. The simplicity of such functional representations can provide an improved performance on fundamental tasks such as noise reduction, imputation and differential expression analysis. However, temporal gene expression profiles are often studied with models that treat time as a categorical variable, neglecting the dependence between time points. Here, we present ImpulseDE2, a framework for differential expression analysis that combines the power of the impulse model as a continuous representation of temporal responses along with a noise model tailored specifically to sequencing data. We compare the simple categorical models to ImpulseDE2 and to other continuous models based on natural cubic splines and demonstrate the utility of the continuous approach for studying differential expression in time course sequencing experiments. A unique feature of ImpulseDE2 is the ability to distinguish permanently from transiently up- or down-regulated genes. Using an in vitro differentiation dataset, we demonstrate that this gene classification scheme can be used to highlight distinct transcriptional programs that are associated with different phases of the differentiation process.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 0305-1048
e-ISSN 1362-4962
Zeitschrift Nucleic Acids Research
Quellenangaben Band: 46, Heft: 20, Seiten: , Artikelnummer: e119 Supplement: ,
Verlag Oxford University Press
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
DOI 10.1093/nar/gky675
Scopus ID 85056596126
Erfassungsdatum 2018-12-05
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