Me trying to keep up with the oscillations discussion in bluesky

I'm happy to share that I've recently started a postdoctoral position in the Priya Rajasethupathy Lab at Rockefeller University!

I'm excited for this new chapter where I'll continue exploring brain intrinsic activity and the rules that govern cognition, now diving deeper into molecular approaches.

For everyone going to #SfN in San Diego

A lot of contributions here, even new ways to define authorship order

The spot of a past lab member usually gets scavenged by other members rather quickly, on a first-come, first-served basis. This time, I'm separating my most valuable possessions and making a raffle so that the other lab members have equal opportunities in the scavenging

Last week I defended my Ph.D. It's a bittersweet moment to say goodbye to a project that has shaped my life for years. Time to look toward what's next.
To everyone who's been part of this journey, thank you.
I also got this slick katana with a manifold engraved in it, from @lukesjulson.bsky.social

Lab care package I keep by my desk at all times

Am I ready to defend my thesis yet

Want to explore gcPCA?
I have prepared a detailed tutorial to help you get started:
github.com/SjulsonLab/generalized_contrastive_PCA/tree/main/tutorial
10/

We packaged everything in the gcPCA toolbox, an open-source package with multiple solutions for different needs:
📂 github.com/SjulsonLab/generalized_contrastive_PCA
- Asymmetric or symmetric, Orthogonal or non-orthogonal, and sparse solutions
👉 Check out Table 1 in the paper for details!
9/

Using the sparse solution for gcPCA, we identified multiple genes previously linked to diabetes to be co-expressed in pancreatic type II diabetes patients
8/

gcPCA allowed us to identify subtle, biologically meaningful patterns. For example, when analyzing pre- and post-learning hippocampal activity, PCA returns components with no apparent task structure. However, gcPCA revealed components with a structure reflecting the task performed.
7/

So we developed gcPCA, a flexible approach that builds on the strengths of cPCA while addressing its limitations.
The key idea?
We add a normalization in the objective function to identify dimensions with the largest relative change in variance between conditions.
6/

But it’s not as simple as just “removing” the hyperparameter. In real-world biological data, especially when sample sizes are small, random fluctuations in high-variance dimensions can overshadow the true signal.
We believe cPCA introduced the α to try to suppress these fluctuations.
5/

That’s when the idea for generalized contrastive PCA (gcPCA) was born.
What if we could:
- Remove the hyperparameter α?
- Make the method symmetric, treating both datasets equally.
4/

We initially tried using contrastive PCA (cPCA), which showed promise. However, it came with a drawback:
- A hyperparameter (α) controls the comparison, and different (α) values give equally probable solutions.
- It uses one experimental condition as a control, creating asymmetric comparisons.
3/

Our team works with high-dimensional datasets, think large-scale electrophysiology, single-cell RNA-seq, etc. In our last project, we hit a wall: how do you compare two experimental conditions (e.g., asleep vs. awake neural activity) when existing tools focus on one dataset at a time?
2/

Does your research involve comparing experimental conditions? Then our latest publication is for you: We developed generalized contrastive PCA (gcPCA), a tool for comparing high-dimensional datasets. 🧠📊 doi.org/10.1371/journal.pcbi.1012747
This tool was born out of necessity, here is the story. 🧵
1/

Doom running on a PDF made my day

github.com/ading2210/do...