In the keynote, Microsoft CVP Joseph Sirosh introduced the "language of data": open source R. Sirosh encouraged the audience to learn R, saying "if there is a single language that you choose to learn today .. let it be R".
The keynote featured a demonstration of genomic data analysis using R. The analysis was based on the 1000 genomes data set stored in the HDInsight Hadoop-in-the-cloud service. Revolution R Enterprise running on eight Hadoop clusters distributed around the globe (about 1600 cores in total), and R's Bioconductor suite (specifically the VariantTools and gmapR packages), was used to perform 'variant calling' and calculate the disease risks indicated by a subset of the 1000 genomes in parallel. The result was an interactive heat-map showing the disease risks for each individual.
The heat map was created by Winston Chang and Joe Cheng from RStudio as an htmlwidget using the D3heatmap package. (You can interact with a variant of the heatmap from the demo here.)
The next part of the demo was to compare an individual's disease risks — as indicated by his or her DNA — to the population. Joseph Sirosh had his own DNA sequence for this purpose, which he submitted via a Windows Phone app to an Azure service running R. This is easy to do with Azure ML Studio: just put your R code as part of a workflow, and an API will automatically be generated on request. In this way you can publish any R code as an API to the cloud, which is then callable by any connected application.
You can watch the entire keynote presentation below, and the R demo begins at around the 23 minute mark.