dawnn
Differential Abundance with Neural Networks
Detects regions of differential abundance in single-cell transcriptomic data by applying a pre-trained neural network model to the labels of each cell's nearest neighbours. Tests for both local and global differential abundance, controlling the false discovery rate with the Benjamini-Yekutieli procedure. The method is described in Hall and Castellano (2023) <doi:10.1101/2023.05.05.539427>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1.1 |
rolling linux/jammy R-4.5 | dawnn_2.1.1.tar.gz |
643.2 KiB |
2.1.1 |
rolling linux/noble R-4.5 | dawnn_2.1.1.tar.gz |
643.2 KiB |
2.1.1 |
rolling source/ R- | dawnn_2.1.1.tar.gz |
993.2 KiB |
2.1.1 |
latest linux/jammy R-4.5 | dawnn_2.1.1.tar.gz |
643.2 KiB |
2.1.1 |
latest linux/noble R-4.5 | dawnn_2.1.1.tar.gz |
643.2 KiB |
2.1.1 |
latest source/ R- | dawnn_2.1.1.tar.gz |
993.2 KiB |
2.1.1 |
2026-04-23 source/ R- | dawnn_2.1.1.tar.gz |
0 B |