Welcome to the R2platform DataScience Annals in 2023

Welcome to the R2platform DataScience Annals. This blog will serve as a low-level entry point to provide some insights into datascience that may also be applicable for users of the http://r2platform.com .

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Plotting updates for the open online R2platform. The data science platform for biomedical researchers

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  Ploting updates for the R2platform We have been working hard to bring you the latest update in the R2 genomics analysis and visualization platform ( https://r2.amc.nl ); the goto web-tool for biomedical researchers to test their hypotheses on public omics data, without the need for bioinformatics or coding expertise. In the latest update, we have made substantial changes to many of the plots that can be produced within R2. Many of the adaptable parameters have now become interactive and are placed under the 'gears' icon. The 'gears' icon Let's have a look at a gene within one of the many resources that are publicly available in R2. We will look at the GATA3 gene in an integrated resouce where samples from both GTEx as well as the TCGA have been integrated and 'batch corrected' to get an immersive view of expression in normal as well as cancer samples. We first select our resource of interest by clicking on 'select a data set'. and then find the ...
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"Who Survives, and Why?" — Using Kaplan-Meier Analysis to Link Gene Expression to Patient Outcomes

 There's a moment in every cancer biologist's research when the question shifts from what is this gene doing? to does it actually matter for patients? That shift used to require a statistician, a clinical dataset, and a lot of back-and-forth. R2 collapses all of that into a single afternoon. The Kaplan-Meier module in R2 is built for exactly this question. A Kaplan-Meier curve is one of medicine's most powerful visual tools — it shows you, over time, what fraction of patients in a group are still alive (or relapse-free). When you split patients into two groups — say, high versus low expression of your favourite gene — and their survival curves diverge dramatically, you feel it in your stomach. That divergence is a signal. Here's how a typical session goes. You've been studying a receptor gene that you suspect is linked to poor prognosis. You open R2, select a neuroblastoma dataset with survival annotation, and navigate to the Kaplan-Meier module. You choose to ...
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"Finding the Axes of Variation" — Understanding Your Data with Principal Component Analysis

 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...
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