Jules Samaran

This study links transcriptomic data with real-time cell behavior to uncover molecular fate asymmetry and a quiescence cycle in adult neural stem cells.

CHAMPOLLION improves multi-omics integration by using regularized optimal transport to align unpaired cells and reveal cross-modal relationships, outperforming existing methods with fewer paired cells.

scPRINT pre-trains on 50 million cells to predict gene networks, outperforming existing methods in inference and zero-shot tasks.

scConfluence improves single-cell diagonal integration by combining autoencoders with regularized Inverse Optimal Transport, outperforming existing methods in three biological applications.

The paper introduces STORIES, a method for learning cell fate landscapes from spatial transcriptomics data, showing improved spatial coherence in neural regeneration and gliogenesis analyses.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.