A free pass/fail class, MATLAB galore, and a winter break that stayed intact.
Date:
Introduction to Spatial Omics (128)
Table of Contents
Computational Biology 101
This was an intercession class—the rare, free, pass/fail type that’s less about mastery and more about giving you a bird’s-eye view. Think sampler platter, not three-course meal.
Why did I take it? Honestly, because I was curious about the PI. The course was actually taught by their PhD student, and I thought it would be fun to get a sneak peek into that lab’s world. Bonus: the class was fully remote. Which meant I didn’t have to cut my winter break short just to trudge back to campus for one lonely credit. Win.
MATLAB. MATLAB. MATLAB.
Did I mention MATLAB? Because that’s basically what the course was. MATLAB for preprocessing. MATLAB for analysis. MATLAB for visualization. If you can dream it, you can plot() it.
Layered on top: some linear algebra greatest hits—PCA, declustering, normalization. The kind of tricks machine learning people like to dress up as “new,” except biostatisticians were doing them before ML had its current hype cycle.
How to Research 101
The part I liked most was how the course framed research thinking. Not just
“Look, a result!”
but also
“Is this actually real, or just domain shift masquerading as discovery?”
I still remember the mock conversation:
“Hey, this is what I got! New result!”
“Have you checked…?”
“Uhh… durhhh…”
Humbling. Useful. A much-needed reminder that flashy findings mean little if they’re not rigorously checked.
Beyond the Class
I picked up more than I expected about spatial transcriptomics—what it is, how people analyze it, and some of the projects the PI’s group had worked on. Later, I even got to interview the PI for the school newspaper, which was a fun full-circle moment. I’m not heading down the computational biology route (at least not right now), but this class planted a seed. You’ll see comp-bio pop up again in my later coursework and projects.