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Jaewon Oh - Week 4

I feel like there are two essential parts of coding. One is coding, and the other is the shameless act of talking to an inanimate object. During the past four weeks I have learnt that coding can be overly frustrating because a single punctation mark may cause your code not to work. In order to get things working, you need to be able to look through your code meticulously. But sometimes that doesn't always work. This week, I tried the "rubber ducking" method, a funny concept where you explain your code line by line to a duck or object of your choice. By doing so you may eventually locate the bug/error in your code. I've been talking to my stapler all week!

The past week and a half, the lab has simply consisted of me, my stapler, and Dr. Cannataro. The three of us worked together to tackle the problem I mentioned in my previous post about gaining multiple RFS values for the same codon numbers + mutations. We decided to average out the RFS values for each mutation before plotting them against the selection intensity and proportion of mutations in specific tumor types. 

The data we were comparing RFS values to consisted of 21 different tumor types. The research where the RFS values were obtained from, however, used cancer cells that were derived from lung cells. So then we only plotted combined RFS data with data on lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). There were no differences in the graphs, but we could safely conclude that a higher fitness score (RFS) led to available selection intensities. 

What Dr. Cannataro and I are now curious about, is why we have selection intensities for certain codon location that had a negative RFS score. Maybe there were other mutations other than p53 that would cause them to be selected for in humans?

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