Waddington Optimal Transport (wot) uses time-course data to infer how the probability distribution of cells in gene-expression space evolves over time, by using the mathematical approach of optimal transport.
If you use this toolkit, please consider reading the citing wot section on how to properly cite it in your work
wot features a command line interface that offers fine-grained control over all calculations.
All of wot tools can be used inside a Python script.
Just import wot and post-process or plot your data directly in Python. Interoperability is ensured between all interfaces, so the data you compute in Python will be cached and can be reused to speed up calculations when using the interactive interface.
Classify your cells based on gene expression, identify new cell sets, compare cell populations with differential expression, track the evolution of a population over time, identify the shared ancestors of two populations, or isolate key marker genes.
The full source code for this package, the examples presented here, and archives with precomputed transport maps on simulated data, along with Python scripts for the most common use cases are available on Github.