This past Thursday, my group had its first annual “Bioinformatics Festival”.

We had some informational talks, software demonstrations, and a “Genius Booth” (yes, we’re mostly Apple fans in our group and we all use Macs, as do many of the researchers within NIAID). It was intended as a “who-we-are-and-what-we-can-do-for-you” pitch and it went rather well.

My role was (supposed to be) minimal, merely being available the last two hours of the day to talk about and demonstrate SPICE 5 to those who are interested. Whether fortunate or not for my employers, I felt a bit more extroverted than usual that day, and I felt bad for having been held up by traffic on I-270 (and subsequently missing a speech by one of the senior management within NIAID). To make up for it, I spent much of the day trying to help ensnare potential collaborators (or, at the very least, free word-of-mouth advertising for our group). Within the first five minutes, I “enlightened” a random passer-by about our general existence and willingness to serve. I felt accomplished.

For being such a niche tool, however, SPICE did quite well, garnering more attention than I expected. A number of people had some basic questions about it and wanted to learn more. It was clear, however, that some were flow cytometry technicians who operated the instrument but had not yet taken a look at the big picture (ie, what the data they were collecting would be used for). Some assumed it was a replacement for FloJo and others thought it was a means to get the data into Excel. I found that last bit both funny and frustrating.

Let me explain that. The “big picture” (from a non-scientist’s perspective and vocabulary) is to test the efficacy of a proposed vaccine, for example. This involves groups being given different vaccination methods (or placebo) and having blood samples collected at set intervals to measure their response. Flow cytometry is how this response is measured. The blood (or tissue) samples are then run through the instrument in order to determine the type of cell and/or the level of cell function. Put simply – probably too simply to be strictly correct – in this case the researchers are measuring the body’s immune response based on how strongly a certain cell is prepared to carry out a certain immune-related function. After this data is processed, you’re left with a large number of individual categorical measurements. For example, “0.026% of the cells were CD8 positive, IL2 negative, from patient 23, three months after the vaccination, via Dryvax method”.

The next step is to analyze this information. You may wish to average all of the patients’ responses to the Dryvax method, overlaid by time point. You may then wish to turn certain functions on or off (such as viewing only CD8-positive measurements, or IL2-negative measurements), or turn the whole analysis on its head and look at the data in a different way. It is this live querying, exploration, and visualization of the data set that SPICE is meant for.

It is difficult (and in many ways impossible) to produce a sufficiently-flexible Excel spreadsheet for any kind of query. It is tedious and time-consuming to tailor a new, information-rich spreadsheet (complete with graphs) for each scenario on a case-by-case basis. In addition, there are some sorts of visual annotation that are specifically useful to the analysis of vaccination trial data but aren’t part of standard charts and graphs. This is where SPICE … well … excels.

All that aside, the “festival” was exhausting (I’m sure much more so for the guy that arranged it all – poor Jason) but it looks to have been a huge success. I enjoyed showing off my hard work and I’m sure my colleagues enjoyed showing theirs off as well. Go team.

Disclaimer: I do not speak for NIAID or the US government. This is an unofficial, personal post about an event in my professional life. Any views expressed herein are my own opinion and do not necessarily reflect those of my employer or NIAID.

Photos by Leo Lu