期刊
GENOME BIOLOGY
卷 15, 期 12, 页码 -出版社
BMC
DOI: 10.1186/s13059-014-0550-8
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资金
- International Max Planck Research School for Computational Biology and Scientific Computing
- National Institutes of Health [5T32CA009337-33]
- European Union's 7th Framework Programme (Health) via Project Radiant
- NATIONAL CANCER INSTITUTE [T32CA009337] Funding Source: NIH RePORTER
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. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.
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