https://github.com/cbg-ethz/scdef

Deep exponential families for single-cell data.
https://github.com/cbg-ethz/scdef

batch-integration gene-signatures hierarchical-models jax matrix-factorization scrna-seq single-cell-rna-seq variational-inference

Last synced: 6 months ago
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Deep exponential families for single-cell data.

• Host: GitHub
• URL: https://github.com/cbg-ethz/scdef
• Owner: cbg-ethz
• Created: 2022-06-08T10:19:35.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-11-13T18:06:49.000Z (11 months ago)
• Last Synced: 2025-04-28T13:09:53.483Z (6 months ago)
• Topics: batch-integration, gene-signatures, hierarchical-models, jax, matrix-factorization, scrna-seq, single-cell-rna-seq, variational-inference
• Language: Python
• Homepage: https://cbg-ethz.github.io/scDEF/
• Size: 35 MB
• Stars: 21
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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Deep exponential families for single-cell data. scDEF learns hierarchies of cell states and their gene signatures from scRNA-seq data. The method can be used for dimensionality reduction, visualization, gene signature identification, clustering at multiple levels of resolution, and batch integration. The informed version (iscDEF) can additionally take known gene lists to jointly assign cells to types and find clusters within each type.

To install scDEF and get started, [read the documentation](https://cbg-ethz.github.io/scDEF).