hehe

This is code for da pengster (bio ML stuffs)

The background for this project is that we are given two datasets:

1. coupled single-cell measurements of the form (DNA, RNA), where DNA is a ~220k dimensional vector of ATAC-seq chromatin accessibility measurements for 220k genes and RNA is a ~24k vector of measured gene expression
2. coupled single-cell measurements of the form (RNA, protein), where RNA is defined as before and protein is a 140 dimensional vector of surface level protein measurements for 140 different proteins

Table of Contents

• jepa.py contains a Joint Embedding (Predictive) Architecture framework that wraps several individual models (joint embeddings work like CLIP or this, but what I am doing has more moving parts)
• models.py contains the actual models implemented (currently Enformer and a better version of dilated nets)
• trainer.py contains a big boy training framework, complete with lr scheduling, patience algorithms, etc.
• datasets.py implements custom PyTorch Dataset objects to allow for efficient dataloading of the massive .h5 files we use
• env_init.sh sets up the python virtual environment to make things nice

The rest of the code is exploratory and not finalized (well, nothing is finalized but you know what i mean)