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From this, independent snapshots are sampled at various temporal instants, each with limited sample resolution (red). From these data, gWOT aims to reconstruct trajectories as a law on paths (blue).\n\n![Example sample path reconstruction](aux_files/illustration.png)\n\nThe underlying model assumption on which gWOT is based is that the generative process is a drift-diffusion process with branching, in which the evolution of any cell over an infinitesimal time is described by the stochastic differential equation (SDE) \n\n![Diffusion-drift SDE](aux_files/sde.png).\n\nCells in this process also divide and die at rates `beta(x, t)` and `delta(x, t)` respectively. Consider the setting where independent snapshots are sampled at many timepoints, but each individual snapshot may only contain partial information due to capturing only a few particles.\n\nFrom this data (red) **gWOT** aims to estimate the underlying stochastic process in the form of a *law on paths* (blue).\n\n## Installation\n\nTo install, use `pip install gwot`.\n\nAlternatively, clone this repository and `cd gWOT \u0026\u0026 pip install .`\n\n## Documentation\n\nRead the [documentation](https://gwot.readthedocs.io/en/latest/) and also refer to the [paper](https://arxiv.org/abs/2102.09204) for mathematical details regarding the method and its implementation.\n\n## Example application 1: bistable landscape with branching\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zsteve/gWOT/blob/main/examples/gWOT_example.ipynb)\n\nThis is a simple example tutorial covering basic application of gWOT to simulated data from a bistable (bifurcating) stochastic process where particles undergo branching (division). \n\n\n## Example application 2: reprogramming dataset (Schiebinger et al., 2019) \n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zsteve/gWOT/blob/main/examples/reprogramming.ipynb)\n\n## Paper\n\nThis code accompanies the paper [(arXiv link)](https://arxiv.org/abs/2102.09204)\n\nLavenant, H., Zhang, S., Kim, Y., \u0026 Schiebinger, G. (2021). Towards a mathematical theory of trajectory inference.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzsteve%2Fgwot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzsteve%2Fgwot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzsteve%2Fgwot/lists"}