Integrating Single-cell Multi-omics Data through Self-supervised Clustering
Stable version:
python==3.8.16
pytorch==1.12.0
scanpy==1.9.3
anndata==0.8.0
episcanpy==0.4.0
Other required python libraries: numpy, scipy, pandas, h5py, networkx, tqdm etc.
Also you can install the required packages follow there instructions (tested on a linux terminal):
conda env create -f environment.yaml
| Dataset | Chen et al. | Cao et al. | PBMC 10K-1 | PBMC 10K-2 | Ma te al. | GSE194122 |
|---|---|---|---|---|---|---|
| #Cell | 1047 | 1621 | 10412 | 11020 | 32231 | 69249 |
| #CellType | 4 | 3 | 19 | 12 | 22 | 23 |
| #Gene | 18666 | 113153 | 36601 | 36601 | 13428 | 23296 |
| #Peak | 136771 | 189603 | 116490 | 344592 | 108377 | 120010 |
| Protocol | SNARE | sci-CAR | 10x | 10x | SHARE | 10x |
For training:
python train5.py -a DATASET_NAME -r default -z 32 --combine concat --gene-loss mse -o output --count-key X