Plot update for the R2platform

 Plot update 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 'TCGA GTEX' using the filters and by pressing 'confirm selection'

Main page from R2

We then select 'view a gene in groups' from the 'select ype of analysis' and progress by clicking 'next'

Selecting the gene, grouping variable and setting other parameters

Here we can then type the gene that we are interested in (the GAT3 gene), and select this from the drop-down that will appear while we are typing. The we select the grouping variable which will be used to seggregate the samples. We select 'tissue_type' here.

From the graphics section, we choose 'violin' to select the violin plot and add scatter to 'true'. We will use 'color' to distinguish between 'tumor' and 'normal' using the grouping variable 'tn'. And then 'submit'.

A violin plot showing the expression of GATA3 in different tissue types, colored by tumor / normal status

The violin plot is now shown separated by the type of tissue, and colored according to tumor or normal status. We can clearly see that breast and bladder have a high expression. If you click on the 'gears' icon, the settings become visible. Here you can export or copy the image to the clipboard, for easy pasting in e.g. PowerPoint.

In the 'general' tab, many settings can be adapted to mold the plot to your linking

For example, you can change the aspect ratio using the dimensions, change font sizes, the representation of the dots (size / opacity / apearance) etc. This gives you geat flexibility.

Since we have used a 'grouping variable' for the color, we can simply click on the legend groups to toggle them. The plot will update accordingly (and also update the test statistic). This is a great way for eploring patterns.

toggling color groups

Using the form that is still available near the bottom of the page, you can change gene, tpye of graph and much, much more to create the representation you need.

Adapted gene / plot / grouping variable

Another cool thing in R2, is that you can typically also quickly go to another module with a simple click. Let's try the 'sample map' on the right side.

This brings you to the 'data-driven' views of this resource, where we can also depict the expression of a gene in the interactive visualization. We can also choose to 'color by track' and see the groups to see that indeed 'tissue' is also separating in the UMAP representation.

We hope that you get the idea and see the possibilities from this small story and that you will also enjoy the new functions that we implemented in R2.

This is just a tiny example. R2 allows you to do many more things such as differential expression, correlations. pca, heatmaps, survival analysis, but also chipseq, mutations, drugtests, whole genome sequencing and much much more. All on thousands of public resources in an online open tool, that does not need any coding or bioinformatics knowledge.

So direct your browser to the open online R2 genomics analysis and visualization platform https://r2.amc.nl and try it yourself.