"Boiling It Down to a Number" — Using Gene Signatures to Score Biological Programmes

 You've identified a set of genes that you believe represents a biological programme — maybe it's a hypoxia response signature from the literature, or a list of targets you identified from your own ChIP-seq experiment, or simply the genes that came out of your differential expression analysis. The question now is: can I reduce all of that complexity to a single meaningful score for each patient?

That's exactly what R2's gene signatures module is designed to do.

In R2, a signature is defined as a collection of genes — grouped together because they share something: a functional role, a genomic location, a co-expression pattern, or a biological programme you've defined yourself. Once you've assembled your gene list and saved it as a signature in R2, the platform can calculate an activity score for that signature across every sample in your dataset.

The mechanics are intuitive. R2 computes a score that reflects the overall expression activity of your gene set in each sample — essentially collapsing dozens of individual gene measurements into a single number. Each sample now carries that number as a new "track," which you can use in any downstream analysis: plotting it against clinical outcomes, correlating it with another gene, splitting samples into high- and low-signature groups for Kaplan-Meier analysis.

The power becomes apparent when you start combining signatures. You might plot your hypoxia signature score on one axis and a proliferation signature score on the other, with each dot a patient sample. The scatter of dots reveals something you couldn't see when you were looking at individual genes: subpopulations of patients who are hypoxic-but-not-proliferative, or proliferative-but-not-hypoxic, or both. Those quadrants often carry distinct clinical outcomes — and now you have the data to show it.

R2 also lets you draw lines between paired samples in an XY plot — before and after treatment, for example — so you can track how each patient moves through signature space as a result of a therapeutic intervention. Watching a cluster of dots migrate from the high-hypoxia quadrant towards low hypoxia after treatment is one of those moments where the biology feels almost visible.

If you don't want to define your own signature, R2 has a library of curated gene sets — from pathway databases, published studies, and gene ontology terms — ready to use. The barrier to entry is genuinely low.

This is Part of an ongoing series on the R2 Genomics Analysis and Visualization Platform, developed at Amsterdam UMC. All analyses can be freely performed at r2.amc.nl. Full tutorials at r2-tutorials.readthedocs.io.