02-{5,7}12 Final Project: Simulating Latent Single-Cell Gene Expression of COVID-19 Patients with Variational Autoencoder and Implications for Augmenting Classification Models
We present a Variational Autoencoder-based simulation of single cell gene expression from healthy and COVID-19 patients' PBMC cells. To demonstrate the utility of this simulation, we build a synthetic dataset seeded from rare cell types. We then train a classifier of ventilation severity and demonstrate that, by augmenting with simulated data, predictive performance declines slightly. However, based on phylogeny tree analysis, the simulated rare cell data does not differ dramatically from the original distribution, as desired. Regardless of these mixed results, we consider our simulation as a solid baseline and a promising future direction to aiding in single cell research of COVID-19.
Pull the code properly
git clone --recursive https://github.com/simonlevine/singlecell-sim-by-VAE.gitInstall dependencies
python -m venv venv
./venv/bin/pip install -r requirements.txt
pip install --user dvcGrab the data
source .envrc && make download_dataReproduce
source .envrc && dvc repro