Journal
GENOME BIOLOGY
Volume 15, Issue 12, Pages -Publisher
BMC
DOI: 10.1186/s13059-014-0550-8
Keywords
-
Funding
- 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
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available