This report will demonstrate the presence of technical bias in several normal tissue RNA-seq experiments and investigate the ability of different normalization methods to address this bias.
Differential gene expression analysis based on the negative binomial distribution
Bioconductor version: Release (3.8)
Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
Author: Michael Love, Simon Anders, Wolfgang Huber
Maintainer: Michael Love <michaelisaiahlove at gmail.com>
Citation (from within R, enter citation("DESeq2")):
Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.
IMGM is your outsourcing partner and advanced genomic service provider for demanding projects with a focus on Metagenomics, Biomarker Discovery, Biodistribution and Pharmacogenetics.