August is here, the summer is coming to an end! The last two weeks have been extremely invigorating and informational. I attended the #GoldenDoorSummit2018, a retreat for Golden Door Scholars. This scholarship is for high performing undocumented/DACAmented undergrad students. I was blessed to have this scholarship during undergrad and have always enjoyed going to the retreats as they are filled with like-minded peers and always has uplifting programming. We heard talks from immigration activist Gaby Pacheco, software engineer Mark Kroh, motivational speaker Charles Hunt, and Carolina Panthers linebacker Thomas Davis.
Right after the GDS Summit, I went straight to Seattle for the Summer Institute in Statistical Genetics. I was very nervous about this trip for a number of reasons.
Flying cross country to a city I have never been.
The possibility that the modules I signed up for we’re going to fly over my head.
Upon landing in Seattle, I was immediately drawn in by the beauty of the architecture and biodiversity. I had no idea how HUGE the city was and thankfully the weather was GREAT.
The modules I took were Integrative Genomics and MCMC for Genetics. I wanted to take Integrative Genomics because I am interested in merging behavioral, ATACseq, and RNAseq data. The course covered basic principles of RNAseq, the importance and different methods of normalization, working with single cell data, and differential expression analysis. My main take away from this course was to explore my data more and have a clear understanding about how the data is distributed. Also, to try different ways of analyzing the data so the results are robust. An actual walk through of best methods of integrating different types of data was not explicitly discussed, but as of right now I think the correct thing to do is to use the covariates in the meta data in differential expression models to either normalize or correct for.
My second module, MCMC for Genetics, was the one I was mostly scared about not understanding. But to my surprise the class started from explaining what a beta distribution all the way up to MCMC.
I have seen this material covered in these module before but not to the depth the modules were taught. My favorite thing about these courses is that the resources for these classes are super accessible. For each module, they have the previous year’s slides and I also heard a rumor about video lectures floating around the internet somewhere. So for the modules I didn’t attend, I can always look back at the materials.
I strongly recommend these workshops to students eager to immerse in these topics. Warning: this workshop is INTENSE, four 90 minute sessions every day from 8:30am to 5pm, promptly followed by socializing with fellow nerds until wee hours of the morning. (also there is no AC in the dorm rooms)
Did I get much sleep? No. Did I learn invaluable techniques and make meaningful connections. Hell yes!
Now I’m focusing on re-analyzing data to incorporate different kinds or normalization techniques and correcting for covariates such as mapping quality, GC bias, and Fraction of reads in peaks(aka FRiP). Also I plan to dive into MCMC literature with the goal of learning how to build gene expression time series models.
I’m excited about this semester, I’m taking a neurobiology class, getting deeper into my research, and my hubby comes back from his deployment! My first year of grad school was ROUGH but this summer has been about exploration, intense learning, and re-invigorating my passions. Second year, here I come!