R2platform Data Science Annals

 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...

 There's a question that keeps coming up in modern cancer biology, and it goes something like this: we know this gene is silenced in these tumours — but why ? Is it a genetic event, a deletion, a mutation? Or is the promoter methylated, the gene quietly switched off by an epigenetic mechanism that leaves no trace in the sequence itself? DNA methylation and gene expression are two different measurements of the same biological reality, made on different platforms, stored in different formats, analysed by different tools — or at least, they used to be. In R2, they can live side by side in the same analysis. The Cross-Platform Integration module is designed for exactly the scenario where the same patient samples have been profiled on multiple technologies: expression measured by RNA-seq or microarray, methylation measured by an Illumina methylation array, perhaps copy number from a third platform. R2 treats these as a "collection" — a set of datasets that share samples in ...

 Before you can interpret expression data, you need to understand the samples it came from. How old were the patients? What were their tumour stages? Was there a relationship between age and stage in this particular cohort? Do the survival outcomes cluster in ways that align — or don't align — with the clinical categories you've been using as grouping variables? These questions live one level below the gene expression data, in the clinical annotation — the metadata that describes your samples. In R2, that annotation is stored as "tracks," and the Annotation Analyses module is where you interrogate those tracks directly, before a single gene comes into view. The module handles three fundamental types of comparison, and it routes you to the right one automatically based on what you're comparing. If you want to know whether two categorical variables co-occur — are MYCN-amplified tumours more likely to be stage 4? — R2 computes a Fisher's exact test and shows ...