R2platform Data Science Annals

Neuroblastoma remains one of the most challenging pediatric cancers to treat. Despite decades of research, high-risk disease still carries a poor prognosis, and finding effective therapies requires a deep understanding of the molecular landscape driving each tumor. To support that mission, we are excited to announce the release of a comprehensive multi-omics dataset covering the most widely used classical neuroblastoma cell lines — an openly accessible resource designed to accelerate discovery across the field. What Is in the Dataset? This resource brings together multiple layers of molecular and pharmacological data for a panel of classical neuroblastoma cell lines. In one place, researchers can now access: Transcriptomics (mRNA expression) Genome-wide gene expression profiles capturing the transcriptional state of each cell line. These data allow researchers to explore pathway activity, subtype classification, and gene regulatory networks. DNA copy number variation Genome-scal...

 Most research stories end with a publication. The best ones end with a treatment. Between those two endpoints lies a long and difficult journey: from a statistically significant finding in a patient cohort, through mechanistic validation, to a druggable target, through preclinical models, to a clinical trial. It's a journey that requires not just good science, but an organised, evidence-based case for why this particular target, in this particular cancer, deserves the investment of a drug development programme. R2's Target Actionability Review (TAR) module is a tool built specifically to support that case-building process — and it is unlike almost anything else in a genomics platform. A TAR is a manually curated, structured literature review focused on a single gene target in a single cancer context. It brings together evidence from multiple domains: the genomic prevalence of alterations in the target, the functional evidence linking it to disease biology, the availability...

 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 cu...

 Gene expression tells you what a cell is doing right now. But cancer is, at its heart, a disease of the genome — of broken chromosomes, rearranged sequences, amplified oncogenes and deleted tumour suppressors. To understand why a gene is expressed the way it is, sometimes you need to see the structural context: the copy number landscape, the chromosomal rearrangements, the mutations that preceded everything else. R2's WGS/NGS integration tools bring whole-genome sequencing data into the same analytical space as expression data, and the entry point is one of the most visually striking displays in the platform: the Circos plot . A Circos plot is a circular representation of the entire genome. Each chromosome occupies an arc of the circle, and lines drawn across the interior of the circle connect genomic regions that have been rearranged relative to each other — translocations, inversions, insertions of one chromosome into another. For complex cancer genomes, these plots can look ...

 Research rarely lives at the level of a single gene for long. Within a few weeks of finding your gene of interest, you're building a panel — related family members, known interactors, upstream regulators, downstream targets. The question shifts from "what is this gene doing?" to "how does the whole set behave together?" R2's Multiple Genes View is built for exactly this moment in a project. Rather than clicking through each gene individually, you type a list — or paste it in from a spreadsheet — and R2 generates a side-by-side expression overview for all of them simultaneously. Each gene gets its own column of dots, arranged by sample, so you can scan across the panel and immediately see which genes are high, which are low, and crucially, whether they go up and down together or in opposition. The track annotation system brings this to life. You split all samples by a clinical variable — tumour subtype, for instance — and suddenly each column of dots is ...

 There's a thought experiment that helps explain what Principal Component Analysis does. Imagine you're trying to describe the differences between a large group of people, and you have a thousand measurements for each person — height, weight, age, dozens of blood markers, hundreds more. That's an impossibly high-dimensional space. PCA's job is to find the most important directions of variation in that space and let you look along those directions instead. In genomics, the same logic applies. You have thousands of gene expression measurements per sample. PCA collapses that complexity into a small number of "principal components" — axes that capture the most variance in the dataset. The first principal component captures the most variation of all. The second captures the most of what remains, and so on. When you plot your samples along the first two or three of these axes, you're looking at a compressed but surprisingly faithful summary of the whole datase...