"Something Changed — But What?" — Finding Differentially Expressed Genes Between Two Groups

 You've done the experiment. Two groups of samples — maybe treated versus untreated, maybe two tumour subtypes, maybe pre- and post-therapy biopsies. The RNA-seq or array data is back from the facility, processed, and sitting in R2 as a dataset. Now comes the real question: what actually changed?

This is the moment that used to separate wet-lab researchers from computational ones. Not anymore.

R2's differential expression tools let you ask, across all genes simultaneously, which ones are significantly up or down between your groups. But rather than dumping an intimidating table of 20,000 rows on you, R2 walks you through it in a structured, visual way.

Start with a single gene to get your bearings. Say you suspect MYCN behaves differently between INSS stage 4 tumours and the rest. You select "View a Gene in Groups," choose your grouping track (tumour stage), and R2 produces a grouped box plot with statistics. It even runs the appropriate statistical test automatically — t-test for two groups, ANOVA for multiple — and displays the p-value right on the figure. You can copy that figure directly into a presentation.

Then you scale up. Switch to "Find Differential Expression Between Two Groups," define your comparison, and let R2 scan the whole transcriptome. What comes back is a ranked list of genes, ordered by statistical significance, with fold changes and p-values. You can filter by expression magnitude, by p-value threshold, by chromosome — however you want to slice it.

But the real power comes next: R2 lets you plug that gene list directly into Enrichr, a pathway enrichment tool, without leaving the platform. You go from "here are 300 differentially expressed genes" to "these genes are overwhelmingly enriched in cell cycle regulation and p53 signalling" in about three clicks. Suddenly, your experiment has a biological narrative.

And if you prefer a visual summary? Ask for the volcano plot. Fold change on the X-axis, statistical significance on the Y. Your hits scatter into the upper corners like stars, and you can click each one to explore it further.

You started the afternoon with a vague sense that something was different between your groups. You end it knowing what, how much, and in which biological context.

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.