- Column describing groups [group]
- Filter out genes which don't have counts in at least this many samples (1-10000) [1]
- P-value cutoff (0-1) [0.05]
- Multiple testing correction (none, Bonferroni, Holm, Hochberg, BH, BY) [BH]
- Dispersion method (common, tagwise) [tagwise]
- Dispersion value used if no replicates are available (0-1) [0.16]
- Apply TMM normalization (yes, no) [yes]
- Plot width (200-3200 [600]
- Plot height (200-3200) [600]

Details

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:

- de-list-edger.tsv: Result table from statistical testing, including fold change estimates and p-values.
- logFC = log2 fold change between the groups. E.g. value 2 means that the expression has increased 4-fold
- logCPM = the average log2-counts-per-million
- PValue = the two-sided p-value
- FDR = adjusted p-value
- de-list-edger.bed: If you data contained genomic coordinates, the result table is also given as a BED file for genome browser use. The score column contains log2 fold change values.
- edgeR_report.pdf: A PDF file containing:
- ma-plot-edger.pdf: MA plot where significantly differentially expressed features are highlighted.
- dispersion-edger.pdf: Biological coefficient of variation plot.
- mds-plot-edger.pdf: Multidimensional scaling plot to visualize sample similarities.
- p-value-plot-edger.pdf: Raw and adjusted p-value distribution plot.

- edger-log.txt: Log file if no significantly different expression was found.

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.

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