I have a RNA-seq dataset without controls (or you can consider them all controls) and I am interested in an unsupervised ranking or clustering of samples with regard to how they are expressing pathways of interest. I am looking to stratify samples in terms of their pathway activity for specific pathways from the PROGENy resource: https://saezlab.github.io/progeny/ . Would you have any recommendations for how to run this analysis?
As you mentioned the PROGENy resource, which predicts pathway activity based on transcription factor activity and target gene expression, you can leverage this tool to estimate pathway activities for your samples. The PROGENy package provides an R implementation that integrates with popular RNA-seq analysis workflows. By utilizing the PROGENy scores, you can stratify samples based on pathway activity and explore the relationships between samples and pathways.
Hi Jan,
You can try PROGENY scores directly for pathways. You can get a pathway activity scores here : https://bioconductor.org/packages/release/bioc/html/progeny.html
You can also consider Gene Set Variance Analysis. https://bioconductor.org/packages/devel/bioc/vignettes/GSVA/inst/doc/GSVA.html This is can be used as a one sample GSEA and you can analyse how the samples are different in your favourite pathways (gene sets) instead of genes. You can find biological outliers or the response to each one compound.
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