DESeq2-package {DESeq2} | R Documentation

## DESeq2 package for differential analysis of count data

### Description

The DESeq2 package is designed for normalization, visualization, and differential analysis of high-dimensional count data. It makes use of empirical Bayes techniques to estimate priors for log fold change and dispersion, and to calculate posterior estimates for these quantities.

### Details

The main functions are:

- `DESeqDataSet` - build the dataset, see tximeta & tximport packages for preparing input

- `DESeq` - perform differential analysis

- `results` - build a results table

- `lfcShrink` - estimate shrunken LFC (posterior estimates) using apeglm & ashr pakges

- `vst` - apply variance stabilizing transformation, e.g. for PCA or sample clustering

- Plots, e.g.: `plotPCA`, `plotMA`, `plotCounts`

For detailed information on usage, see the package vignette, by typing `vignette("DESeq2")`, or the workflow linked to on the first page of the vignette.

All software-related questions should be posted to the Bioconductor Support Site:

https://support.bioconductor.org

The code can be viewed at the GitHub repository, which also lists the contributtor code of conduct:

https://github.com/mikelove/tximport

### Author(s)

Michael Love, Wolfgang Huber, Simon Anders

### References

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

[Package DESeq2 version 1.27.31 Index]