MARGARET is a Metric learning-based graph-partitioned trajectory inference method for single-cell sequencing data.
MARGARET is a Trajectory Inference (TI) method that utilizes a deep unsupervised metric learning-based approach for inferring the cellular embeddings and employs a novel measure of connectivity between cell clusters and a graph-partitioning approach to reconstruct complex trajectory topologies. MARGARET also utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable absorbing Markov Chain model.