## RNA-seq / Differential expression using edgeR for multivariate experiments

### Description

This tool performs a multivariate analysis for differential expression using the edgeR Bioconductor package.

### Parameters

- Apply normalization (yes, no) [yes]
- Main effect 1
- Main effect 2
- Main effect 3
- Treat main effect 1 as factor (yes, no) [no]
- Treat main effect 2 as factor (yes, no) [no]
- Treat main effect 3 as factor (yes, no) [no]
- Interaction (main, all) [main]

### Details

Given an input table of raw counts, the edgeR package performs statistical analysis to identify differentially expressed genomic features (genes, miRNAs,...) using a multivariate regression model. A maximum of three different variables, and their interactions can be specified for the model. The experiment conditions to be compared should be defined in the phenodata.tsv file, and the appropriate column selected using the "Column describing groups" parameter.

It is highly recommended to always have at least two biological replicates for each experiment condition.

### Output

The analysis output consists of the following files:

- de-list-edger.tsv: Table containing the results of the statistical testing, including fold change estimates and p-values.

### References

This tool uses the edgeR package for statistical analysis. Please read the following article for more detailed information:

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.