https://github.com/keiserlab/exceiver
https://github.com/keiserlab/exceiver
Last synced: 6 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/keiserlab/exceiver
- Owner: keiserlab
- License: mit
- Created: 2022-09-30T19:43:48.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-06T15:08:09.000Z (almost 2 years ago)
- Last Synced: 2024-11-03T03:26:59.396Z (6 months ago)
- Language: Python
- Size: 3.91 MB
- Stars: 11
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# exceiver
[](https://arxiv.org/abs/2210.14330)
A pretrained single cell gene expression language model.
## install
1. clone this repository: `git clone [email protected]:keiserlab/exceiver.git`
2. install lightweight packaging tool: `conda install flit`
3. install this repo in a new environment: `flit install -s`
## usage
see `notebooks/example.ipynb` for loading pretrained models:
```
from exceiver.models import Exceiver
model = Exceiver.load_from_checkpoint("../pretrained_models/exceiver/pretrained_TS_exceiver.ckpt")
```
## preprocessing
1. downlaod the Tabula Sapiens dataset [from figshare](https://figshare.com/articles/dataset/Tabula_Sapiens_release_1_0/14267219), specifically `TabulaSapiens.h5ad.zip` (careful this link will likely begin download: https://figshare.com/ndownloader/files/34702114)
2. run `scripts/preprocess.py`:
```
python preprocess.py --ts_path /path/to/download/TabulaSapiens.h5ad
--out_path /path/to/prepocessed/TabulaSapiens
```
## training
`pytorch_lightning` makes distributed training easy and CLI access to a host of hyperparameters by running `scripts/train.py`:
```
python train.py --name MODELNAME
--data_path /path/to/prepocessed/TabulaSapiens
--logs path/to/model/logs
--frac 0.15
--num_layers 1
--nhead 4
--query_len 128
--batch_size 64
--min_epochs 5
--max_epochs 10
--strategy ddp
--gpus 0,1,2,3
```