edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
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.
- Dataset: 2 sample groups.
- Method: The inference algorithm:
- Common Dispersion:
Common dispersion for all the tags is estimated using the quantile-adjusted
conditional maximum likelihood (qCML) method.
- 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
- 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
- Load RNAseq type data
- Launch edgeR from ToolBar -> Statistics -> edgeR
- Assign samples to 2 groups or leave them out
- Choose inferecne method
- Choose significance coutt-off method (p-value or fdr) and specify a value
- Hit OK