A couple years ago I started becoming obsessed with the opioid epidemic. I spend a non-trivial amount of my time thinking about it and if I am ever scrambling for a topic in a social situation, it ends up being pretty much the only thing I can think of. (Because I am So Smooth.) As a public health devotee, the epidemic hits all of my passions. To name just a few: Issue exacerbated by outdated stigma? Check. Multiple demographics impacted? Big time. Heartbreaking narratives? You will never stop crying. Socioeconomic confounders? And how.
Recently, alarm over the epidemic has reached such a fever pitch that various agencies have started hosting opioid-crisis-focused datathons/codathons/hackathons. (Three different new words for essentially the same concept does seem a little excessive, I agree.)
Being a data scientist is pretty much the coolest thing, and the world seems to have caught on to this fact. This means that there is more competition to do the really interesting work, but it also means that there is a critical mass of data scientists who will be interested in sciencing together for a marathon period (24 hours straight at least) on particular topics in our personal time. I love attending them. They are a great opportunity to build less-exercised skills through a sudden flood of experience hours. So, you can just imagine how I feel about getting access to new opioid epidemic data as part of a hackathon. To spell it out: Teamwork + Opioid Epidemic + Data + Hackathon = G.O.A.T.
I have participated in 2 Opioid Hackathons in the past 6 months, and I and some of my coworkers are planning one of our own, sponsored by our company. One of my kick-ass data science colleagues, Catherine Ordun, submitted our results for these events in an abstract to the International Society for Disease Surveillance (ISDS) Conference in Orlando this year, and we are presenting tomorrow! I’ll be back to post more about the topic (unless I take another 3 year hiatus, obvi.)
This past year I got involved with Software Carpentry, a group which teaches basic research computing to scientists. I’ve helped Stephen Turner teach a few RNA-Seq workshops through UVA’s BioConnector, and last March I taught my first independent-from-Stephen workshop with two other UVA instructors, Alex Koeppel and Zhuo Fu.
Teaching basic shell programming!
Look, up here at this line!
We had such a good time working together and with Bart Ragon and VP Nagraj from the UVA Health Sciences Library that we decided to keep meeting in this awesome HSL room every week.
Yes, that’s TWO screens for coding.
Doesn’t it look like the bridge of the Enterprise? Make it so.
At first we decided to keep meeting in order to review and debug each other’s code, but then I had a brainwave. Perhaps we could work on an independent project together?
We all have a basic familiarity with programming in R, so that seemed like the language of choice. At first I had envisioned some interactive modifications to LocusZoom
— something along the lines of an app where you can learn more about particular SNPs by clicking or hovering over their representations on a graph. However, upon sober reflection, I realized that I am the only one in the main group that works in genomics, and the required amount of domain-specific background knowledge would be extremely high. Additionally, fiddling with such a feature rich program like LocusZoom might not make for a great starter project.
As part of my position at Public Health Sciences I work with both the Center for Public Health Genomics (CPHG) and the Institute of Law, Psychiatry and Public Policy (ILPPP). The specific project that I work on involves analyzing court data pertaining to mental health proceedings in the State of Virginia. It’s a very different domain from my other bioinformatics work, but it ends up being a perfect fit as a project to cut our teeth on app construction with R. The data tables are fairly straightforward, even if deeper understanding requires further domain knowledge. Also, there would be actual immediate public policy benefits to having interactive and layered representations of the data. (e.g. allowing lawmakers to see up-to-date graphs of commitment trends in their specific districts.)
So, from now on, the inter-departmental SWC project group (cool nickname pending), along with my colleague, Ashleigh Allen from the ILPPP, will be spending weekly meetings brainstorming/planning/building an app. We’re researching various R packages to help us toward our goal. Right now we are considering shiny.
I’ll keep both of my readers up to date on our project as it takes shape!
I’ve been horribly neglecting this blog as well as ThisScienceLife, and that’s totally going to change. In the meantime, I wanted to tell all three of you that I’m an author on a paper in Nature Genetics, published today.
I *SAID* ‘Nature Genetics’.
If you would like to read it or gaze adoringly at my name, it’s called Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers, and it’s right here.
This is how we are over here right now.