This tool takes as input a table of raw counts from the different samples. The count file has to be associated with a phenodata file describing the experimental factors. These files are best created by the tool "Utilities / Define NGS experiment", which combines count files for different samples to one table and created a phenodata file for it.
Trimmed mean of M-values (TMM) normalization is used to calculate normalization factors in order to reduce RNA compositon bias (which can arise for example when a small number of genes are very highly expressed in one experiment condition but not in the other).
Dispersion is estimated using Cox-Reid profile-adjusted likelyhood (CR) method. Trended dispersions are estimated prior to estimating tagwise dispersions.
Statistical analysis to identify differentially expressed genomic features (genes, miRNAs,...) is performed using a multivariate regression model. A maximum of three different variables, and their interactions can be specified for the model. It is highly recommended to always have at least two biological replicates for each experiment condition.
This tool uses the edgeR package for statistical analysis. Please cite the following articles:
MD Robinson, DJ McCarthy and GK Smyth. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26 (1):139-40, Jan 2010.
DJ McCarthy, Y Chen and GK Smyth. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res, 40 (10):4288-97, May 2012.