Marius Lange

RegVelo integrates gene regulatory networks with dynamic modeling to predict cellular changes and perturbations, showing improved performance in cell cycle, hematopoiesis, and pancreatic development datasets.

moscot unifies optimal transport for temporal, spatial and spatiotemporal single-cell mapping, using multimodal cost spaces and low-rank solvers to scale to 1.7 million mouse embryo cells.

The paper introduces moslin, a method that combines lineage and gene expression data across time points to predict cell fate and decision driver genes.

The paper introduces CellRank 2, a framework for analyzing multiview single-cell data to study cellular fate, showing it recovers terminal states and fate probabilities across modalities in hematopoiesis and endodermal development.

CellRank maps cell fates in single-cell RNA sequencing data by integrating RNA velocity and trajectory inference, revealing novel regeneration trajectories in lung injury.

scVelo fits a likelihood-based model of full splicing kinetics, extending RNA velocity to transient cell states and adding gene-specific kinetic rates, a latent time and putative driver genes.

scSLAM-seq measures transcription dynamics in single cells by distinguishing newly synthesized from pre-existing RNA, revealing stochastic transcription and gene-specific differences.