SIGRN: Inferring Gene Regulatory Network with Soft Introspective Variational Autoencoders
Rongyuan Li, Jingli Wu, Gaoshi Li, Jiafei Liu, Jinlu Liu, Junbo Xuan, Zheng Deng · 2024
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Description
Rongyuan Li and co-authors present SIGRN, a method to infer gene regulatory networks from single-cell data, using a soft introspective variational autoencoder. Gene networks reveal how genes switch each other on and off. Inferring them from sequencing data is cheap next to lab mapping, but standard ones generate poor-quality synthetic data.
An adversarial mechanism was added to sharpen the autoencoder. The soft mode avoids extra networks and parameters. A structural equation model carries the regulatory links. Benchmark tests pitted SIGRN against nine leading methods, and it won on accuracy across most datasets used. How well real cell data was captured also improved. The tool serves biology and disease studies.
Catalog record
| CREATOR | Rongyuan Li, Jingli Wu, Gaoshi Li, Jiafei Liu, Jinlu Liu, Junbo Xuan, Zheng Deng |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |