The ability to instantaneously switch between operating systems and system configurations.Then scaling down to save costs once you are done.

The ability to instantaneously augment your CPU and memory to any imaginable configuration just by clicking a menu.The ability to do your analysis from any device, be it a PC, tablet or even smartphone.I will also note that if statisticians do not embrace these new-found powers, we should not be surprised if data analysis becomes synonymous with Machine Learning and not with Statistics (if you have no idea what I am talking about, read this excellent post by Larry Wasserman).Īs motivation for analysis in the cloud consider: I fell in love with my new cloud powers and I strongly recommend all statisticians and data scientists get friendly with these services. Storing your data and analyzing it on the cloud, be it AWS, Azure, Rackspace or others, is a quantum leap in analysis capabilities. While imaging genetics is an interesting research topic, and the hackathon was a great idea by itself, it is the AWS I wish to present in this post. We badly needed this computing power, as we had 14*10 9 p-values to compute in order to localize genetic associations in the brain leading to Figure 1.įigure 1- Brain volumes significantly associated to genotype. Being supported by Amazon, the hackathon participants were provided with Amazon credit in order to promote the analysis using Amazon’s Web Services (AWS). I have recently had the delight to participate in a “Brain Hackathon” organized as part of the OHBM2013 conference. If I did not mention an important tool or package I apologize, and invite readers to contribute in the comments. This post is not intended to be a comprehensive review, but more of a “getting started guide”.

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