Differential expression analysis for sequence count data
Motivation: High throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA Seq) protein DNA binding (ChIP Seq) cell counting. Statistical inference of differential signal in these data needs to take into account their natural variability throughout the dynamic range. When the number of replicates is small, error modeling is needed to achieve statistical power. Results: We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type I error and provides good detection power.Availability: A free open source R/Biondonductor software package, called "DESeq" is available from "http:/www huber.embl.de/users/anders/DESeq":http:/www huber.embl.de/users/anders/DESeq