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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50412

Love,  Michael I.
IMPRS for Computational Biology and Scientific Computing - IMPRS-CBSC (Kirsten Kelleher), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Love.pdf
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Zitation

Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15: 15:550. doi:10.1186/s13059-014-0550-8.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0025-0992-5
Zusammenfassung
In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.