edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Description

edgeR is a Bioconductor software package for examining differential expression of replicated count data. An over-dispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of over-dispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated.

Parameters

1. Dataset: 2 sample groups.
2. Method: The inference algorithm:
   1. Common Dispersion: Common dispersion for all the tags is estimated using the quantile-adjusted conditional maximum likelihood (qCML) method.
   2. Moderated Tagwise Dispersion: Separate estimate of dispersions for individual tags using qCML. As individual tags typically don't provide enough data to estimate the dispersion reliably, edgeR implements an empirical Bayes strategy for squeezing the tagwise dispersions towards the common dispersion. The amount of shrinkage is determined by the prior weight given to the common dispersion and the precision of the tagwise estimates.
3. Output: edgeR creates a standard MeV viewer nodeo nthe left tree which conists of heatmaps and tables of both significant, non-significant and a combined list of genes.

How to Run edgeR

1. Load RNAseq type data
2. Launch edgeR from ToolBar -> Statistics -> edgeR
3. Assign samples to 2 groups or leave them out
4. Choose inferecne method
5. Choose significance coutt-off method (p-value or fdr) and specify a value
6. Hit OK