Differential expression using edgeR


Differential expression analysis using the exact test of the edgeR Bioconductor package. Please note that this tool only does a pairwise comparison of two groups (the "classic" approach in the edgeR user guide, see chapter 3.2.2). This tool takes as input the counts table and a phenodata file, where the sample groups are described. You can generate these files using the Define NGS experiment tool. We recommend marking the control group with 0 and the other group with 1 in the phenodata column (by default, this is the group column): the group with the smaller number (or, if using letters, the alphabetically first letter) is used as the baseline. This means that in the results table, positive fold changes are upregulated and negative downregulated genes compared to the control group. For more complex comparisons, or multifactor experiments, you can use the Differential expression using edgeR for multivariate experiments tool, which uses generalized linear models -based statistical methods ("glm edgeR", see the edgeR user guide for more information).



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 groups. These files are best created by the tool "Utilities / Define NGS experiment", which combines count files for different samples to one table, and creates a phenodata file for it.

You should set the filtering parameter to the number of samples in your smallest experimental group. Filtering will cause those genes which are not expressed or are expressed in very low levels (less than 5 counts) to be ignored in statistical testing. These genes have little chance of showing significant evidence for differential expression, and removing them reduces the severity of multiple testing correction of p-values.

Trimmed mean of M-values (TMM) normalization is used to calculate normalization factors in order to correct for different library sizes and to reduce RNA composition effect, which arises when a small number of genes are very highly expressed in one experiment condition but not in the other.

Dispersion means biological coeffient of variation (BCV) squared. E.g. if genes expression typically differs from replicate to replicate by 20% its BCV is 0.2, and its dispersion is 0.04. EdgeR estimates dispersion from replicates using the quantile-adjusted conditional maximum likelyhood method (qCML). Common dispersion calculates a common dispersion value for all genes, while the tagwise method calculates gene-specific dispersions. It uses an empirical Bayes strategy to squeeze the original gene-wise dispersions towards the global, abundance-dependent trend.

You should always have at least three biological replicates for each experiment condition. If you don't have replicates, you can still run the analysis by setting the dispersion value manually with the 'Dispersion value' parameter. The default value for this parameter is 0.16. This corresponds to BCV of 0.4 which is typical for human data (where genes expression can differ from replicate to replicate by 40 %). Typical BVC for genetically identical model organisms is 0.1, so you should set the dispersion guess at 0.01 for this kind of data.

Once negative binomial models are fitted and dispersion estimates are obtained, edgeR proceeds with testing for differential expression using the exact test, which is based on the qCML methods.


The analysis output consists of the following files:


This tool uses the edgeR package for statistical analysis. Please read the edgeR user guide and 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.