Method for simulating single cells using longitudinal scRNA-seq.
Project description
prescient
Software for PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative model for modeling single-cell time-series.
- Current paper version: https://www.biorxiv.org/content/10.1101/2020.08.26.269332v1
- For paper pre-processing scripts, training bash scripts, pre-trained models, and visualization notebooks please visit https://github.com/gifford-lab/prescient-analysis.
Documentation
Documentation is available at https://cgs.csail.mit.edu/prescient.
Requirements
- pytorch 1.4.0
- geomloss 0.2.3, pykeops 1.3
- numpy, scipy, pandas, sklearn, tqdm, annoy
- scanpy, pyreadr, anndata
- Recommended: An Nvidia GPU with CUDA support for GPU acceleration (see paper for more details on computational resources)
Bugs & Suggestions
Please report any bugs, problems, suggestions or requests as a Github issue
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
prescient-0.1.0.tar.gz
(13.7 kB
view hashes)
Built Distribution
prescient-0.1.0-py3-none-any.whl
(17.7 kB
view hashes)
Close
Hashes for prescient-0.1.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42c9482826b78138a3bc522284eec62a6d7885c8e6132cbb707bbf64b96d9cae |
|
MD5 | 7b8431b47de6ea401e029ad96b7d7b5a |
|
BLAKE2b-256 | f9edadaf5ab07c22195b7592af7ec477df77aa70c30415c6cd19186e6161a677 |