"Two Truths About the Same Sample" — Integrating Expression and Methylation Data

 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 common — and lets you draw connections between the different data types as if they were all part of one experiment.

The most direct application is correlation. You want to know: for the 23 neuroblastoma samples profiled on both expression and methylation arrays, does methylation at the promoter of your gene of interest correlate negatively with its expression? R2 lets you plot this directly — methylation level on one axis, expression on the other, one dot per patient — and computes the correlation with statistics. A strong negative correlation is the epigenetic silence signal you were looking for.

But the integration goes further than any single gene. You can ask which genes show the strongest expression-methylation anti-correlation across your cohort — a systematic search for epigenetically silenced genes that doesn't require you to have a candidate in mind. The results are a list of biological hypotheses, each one pointing to a gene whose regulation may be driven by methylation rather than genetic alteration.

For wet-lab biologists, the value is immediate and practical: it tells you which genes are worth treating with demethylating agents in your cell lines, which promoters deserve a bisulphite sequencing experiment, and which silencing events might be reversible rather than genetic.

Two platforms. One story. R2 helps you read them together.

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.