DESeq2-package: DESeq2 package for differential analysis of count data

In DESeq2: Differential gene expression analysis based on the negative binomial distribution

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 contributor 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

DESeq2 documentation built on Feb. 22, 2021, 10 a.m.