"Making It Yours" — Uploading Data, Building Tracks and Collaborating in R2

 At some point, the public datasets stop being enough.

You've spent months characterising a cohort of your own — patient samples collected through your clinical network, cell lines you've treated and profiled, an in vivo experiment that generated a dataset unlike anything in the public domain. The analysis tools in R2 are exactly what you need. But your data isn't there yet.

The Adapting R2 tools are what bridge that gap — and they're more capable than most users realise.

Uploading your own dataset to R2 is a structured process, but a manageable one. You prepare your expression matrix and sample annotation file in the required formats, submit them through the platform's data addition workflow, and within a defined turnaround time, your dataset is live in your private R2 workspace — fully accessible through every analysis module the platform offers, while remaining invisible to anyone outside your authorised group.

Once your data is in, you can enrich it with custom tracks — new annotation layers built from any source you choose. Maybe you have treatment response data that didn't come with the original dataset. Maybe you've manually curated a set of clinical features from patient records. Maybe you've run an immunohistochemistry panel and want to bring those protein-level scores into the genomic analysis. All of this can be uploaded as tracks, turning raw annotation data into analysis-ready grouping variables.

You can also build custom genesets — curated lists of genes that reflect your specific biological interests. A list of targets from your ChIP-seq experiment. A set of genes you've validated in the lab. A signature from a paper your group published five years ago. Once saved as a geneset in R2, these lists become available across all analysis modules — in heatmaps, signature scoring, pathway enrichment, and more.

And when you're working as part of a team, the community features let you share tracks, genesets and datasets with specific collaborators or consortium members. The annotation you built becomes available to your collaborators in Amsterdam, Boston or Tokyo, without any of it becoming public. R2 scales from the individual researcher to the international consortium.

The platform was built on public data, but it was designed to hold your own.

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.