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)
Dawnn is a method to detect differential abundance in a single-cell
transcriptomic dataset.
For installation and usage instructions, see Dawnn's documentation.