Research Post # 3
It’s Thursday, so that means I’m wrapping up my third week at the NYU internship! This week I had to code a visualization of the data (in graphical representation). I used the module NetworkX in python to code the graph. I took Theoretical Computer Science last year and we did a lot of graph theory and other theoretical concepts. I thought those concepts could only be applied to obscure fields, however, that class helped me build a strong understanding of the concepts behind data flow and data structures. So, when I had to code a visual representation of the data, it was surprisingly easy, since I understood how it worked.
I really enjoyed just coding for three or four hours straight. It gave me a lot of time and opportunities to just experiment with the code and try new things and conceptualize what was going on. I ultimately ended up with a graph with 1,000 edges and 1,462 nodes. Apart from just coding a visualization of the data, I also coded an adjacency matrix with all the nodes and played around with that. I was surprised to find that it was really sparse (primarily 0s), but in retrospect it does make sense for an adjacency matrix of 1462x1462 to have mostly 0s.
My professor suggested an interesting idea that instead of just coding the graph, I could also find a way to color the adjacency matrix itself based on the connections and see if there is any pattern there - this is another way of visualizing the data. My professor also made a few other suggestions regarding my graph, including changing the edges’ colors and widths as per different attributes, weighting the edges. He also said that instead of making the nodes red/blue (to represent diseased/non-diseased) I could make them different shades - a darker shade of red representing a higher likelihood that the gene actually is disease-associated, and a lighter shade of red representing a lower likelihood that the gene is disease-associated. I’ve found a feature on NetworkX that I might be able to use for this, so I will try it out and we’ll see.
Something else that I have found is that, in research, you need to accept that you know very little, if anything. The entire point of the field of research is to dive into the unknowns and discover something new. Adapting to this kind of environment proved to be a slightly difficult for me at first. In school, you are expected to always know the answer and outperform your peers by studying harder and learning more. On the contrary, this lab’s environment is completely different from school - there is no competition for getting good grades or more points - everyone is working together and trying to help their peers succeed. No one knows all the answers. The students in the lab and the professor are constantly bouncing ideas off each other, trying to learn and optimize the outcome. I have been learning so much at the lab, not only about neuroscience and machine learning, but also how to approach problems and work in a team.
That’s all I have for this week.