Differential expression analysis for sequence count data.
High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
Anders
Simon
S
European Molecular Biology Laboratory, Mayerhofstraße 1, 69117 Heidelberg, Germany. sanders@fs.tum.de
Huber
Wolfgang
W
D000818:Animals; D016010:Binomial Distribution; D047369:Chromatin Immunoprecipitation / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D004330:Drosophila / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D016014:Linear Models; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D013234:Stem Cells; D046509:Tissue Culture Techniques