DESeq2

The algorithm details for DESeq2 can be found at the external DESeq2 documentation page.

If the value of the raw count includes a decimal fraction, the value will be rounded to an integer before DESeq2 is performed. Before you run this task, we recommend that you first remove (filter out) features expressed at a low level and then perform normalization using Median ratio (DESeq2 only).

Note: DESeq2 differential analysis can only be performed on the following normalization output data node, those methods can produce library sizes:

TMM, CPM, Upper Quartile, Median ratio, Postcounts

Configuring DESeq2

Categorical and numeric attributes, as well as interaction terms can be added to the DESeq2 model. The DESeq2 configuration dialog for adding attributes and interactions to the model is very similar to the ANOVA configuration dialog.

In DESeq2 advanced options configure dialog, there is reference selection option:

A reference level is specified for each categorical factor in the model and the result may be dependent on the choice. In R, the reference level is typically chosen by default whenever a categorical factor is present in the model. This Connected Multiomics option was created to allow the user to specify exactly the same reference level as in the R script, if need be e.g. compare the results with R.

DESeq2

The report produced by DESeq2 is similar to the ANOVA report; each row is a feature and columns include p-value, FDR p-value, and fold change in linear scale for each contrast.

Fold change shrinkage in DESeq2

In R, shrinkage of log2 fold changes is a separate step performed by lfcShrink() function. In Connected Multiomics, it mplements the shrinkage method corresponding to “ashr” option in lfcShrink(). The default shrinkage option in lfcShrink is “apeglm”, but the default method is unable produce results for some comparisons whereas “ashr” has no restrictions. The fold change shrinkage results are produced in “Shrunken Log2(Ratio)” and “s-value” columns in DESeq2 task project report.

Troubleshooting

In addition to the issues addressed in Differential Analysis, DESeq2 may generate missing values in the multiplicity adjustment columns (such as FDR) if "independent filtering" is enabled in Advanced Options:

"Independent filtering" tries removing some features with low expression in order to increase the statistical power. For such removed features, the p-value is reported but FDR and similar multiplicity adjustment measures are set to "?". In order to avoid the missing values in the report, set the option to "No".

References

Love MI, Huber W, and Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014;15(12): 550.