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 [![Tests](https://github.com/cantinilab/stories/actions/workflows/main.yml/badge.svg)](https://github.com/cantinilab/stories/actions/workflows/main.yml)
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 [![Documentation Status](https://readthedocs.org/projects/stories/badge/?version=latest)](https://stories.readthedocs.io/en/latest/?badge=latest)
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 STORIES is a trajectory inference method capable of learning a causal model of cellular differentiation from spatial transcriptomics through time using Fused Gromov-Wasserstein. STORIES learns a potential function that defines each cell's stage of differentiation and allows one to predict the evolution of cells at future time points. In addition, STORIES uncovers possible driver genes and transcriptional regulators of cellular differentiation.