https://github.com/scvae/scvae

Deep learning for single-cell transcript counts
https://github.com/scvae/scvae

deep-learning genomics machine-learning single-cell

Last synced: 3 months ago
JSON representation

Deep learning for single-cell transcript counts

• Host: GitHub
• URL: https://github.com/scvae/scvae
• Owner: scvae
• License: apache-2.0
• Created: 2017-02-13T11:42:21.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-06-30T07:08:00.000Z (almost 4 years ago)
• Last Synced: 2024-02-21T03:21:59.128Z (4 months ago)
• Topics: deep-learning, genomics, machine-learning, single-cell
• Language: Python
• Homepage:
• Size: 2.12 MB
• Stars: 77
• Watchers: 5
• Forks: 25
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE.txt

Lists

• awesome-biomedical-machine-learning - scVAE: Variational auto-encoders for single-cell gene expression data

README

        
# scVAE: Single-cell variational auto-encoders #

scVAE is a command-line tool for modelling single-cell transcript counts using variational auto-encoders.

Install scVAE using pip for Python 3.6 and 3.7:

	$ python3 -m pip install scvae

scVAE can then be used to train a variational auto-encoder on a data set of single-cell transcript counts:

	$ scvae train transcript_counts.tsv

And the resulting model can be evaluated on the same data set:

	$ scvae evaluate transcript_counts.tsv

For more details, see the [documentation][], which include a user guide and a short tutorial.

[documentation]: https://scvae.readthedocs.io