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PROGENy provides high-quality cancer pathway signatures that are based on "pathway responsive genes". This is superior over using e.g. KEGG pathways as the signatures are derived from perturbation experiments. As opposed to genes in a pathway (which might not change expression but just be phosphorylated), we actually know that the signature gene change on the mRNA level.
Dorothea may be useful for all sorts of samples, PROGENy is mostly useful in the field of (immuno-)oncology, but it could still make sense to include it as an optional step in the pipeline.
Possible implementation
Use decoupler (there is both an R version and a Python version) to compute Dorothea and Progeny scores based on the results of the differential gene expression analysis. Include plots such as this one in the MultiQC report:
This would need to be implemented as a non-local module first (i.e. PR'd to https://github.com/nf-core/modules), then integrated into the workflow in a process upstream of the reporting step.
Description of feature
Dorothea may be useful for all sorts of samples, PROGENy is mostly useful in the field of (immuno-)oncology, but it could still make sense to include it as an optional step in the pipeline.
Possible implementation
Use decoupler (there is both an R version and a Python version) to compute Dorothea and Progeny scores based on the results of the differential gene expression analysis. Include plots such as this one in the MultiQC report:
(from https://decoupler-py.readthedocs.io/en/latest/notebooks/bulk.html)
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