This repo is the final project for CS280: Deep generative model for auto-annotation in single-cell analysis. This project implemented a semi-supervised learning extended generative model for single-cell cell type annotation.
Compared with the SCANVI model, we adopted the idea of WAE. The code structure is mainly inspired by scvi
python annotationtest.py
| para | default | usage |
|---|---|---|
| -e | 100 | epochs to run |
| -f | 'simualtion_3.loom' | filename of dataset |
| -n | 10 | labeled cell number |
| -p | 'y' | weather to plot the figs |
| -t | 1 | times to run the experiment |
data folder contaion two of our datasets used in the experiments: simulation_3.loom and high_data_loom.loom.
The high_data_loom.loom is a dataset of mouse cells from different tissues which we merged by ourselves.
The simulation_3.loom is a simulation dataset provided by scvi.
-annotationtest.py: the script to test annoation performance
-dgm4sca
|--dataset: scripys to load data
|--inference: scripts to classify cell type by posterior inference
|--models: scripts about generative model
-data: folder of data files in .loom format. (simulation_3.loom, high_data_loom.loom)