PyPI 官网下载 | wot-0.5.4.tar.gz

# WOT: Waddington-OT # Waddington-OT 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 (OT). ## Install ## Waddington-OT depends on [Python 3](https://www.python.org/downloads/). ### Dependencies ### You can install dependencies for **wot** with [conda](https://conda.io/docs/) : ```sh conda install numpy pandas h5py cython scikit-learn scipy matplotlib conda install -c conda-forge pot ``` ### Install the **wot** package ### ```sh pip install --user wot ``` ## Usage ## ### Initializing an OT Model ### **wot** uses an OTModel as its interface for computing transport maps. You can initialize an OT Model in python with : ```python ot_model = wot.ot.initialize_ot_model('matrix.txt', 'days.txt') ``` All Optimal Transport parameters can be customized when initializing the model. For instance, you could explicitely specify the defaults : ```python ot_model = wot.ot.initialize_ot_model('matrix.txt', 'days.txt', tmap_prefix='tmaps', epsilon=.05, lambda1=10, lambda2=50, batch_size=50, tolerance=1e-2) ``` You can compute all transport maps with : ```python ot_model.compute_all_transport_maps() ``` ### Loading Transport Maps ### Once the transport maps have been created, you can operate on the transport maps using the TransportMapModel interface : ```python tmap_model = wot.tmap.TransportMapModel.from_directory('.') ``` All previously computed transport maps will be available. ### Changing parameters ### If you want to keep the previously computed transport maps, simply initialize a new model with a different prefix. Any model will only affect files that use its `tmap_prefix`, there is no interaction between models with different prefixes. ### Using wot.commands ### All data-processing functions are located in the `wot.commands` subpackage. These include : - ancestor census - gene set scores - gene regulatory networks (grn) - local enrichment - optimal transport validation - trajectory - trajectory trends - convert matrix - force layout - wot server (interactive version of **wot**) All of these are documented on [wot's github pages website](http://broadinstitute.github.io/wot), with examples using simulated data to show how to use and plot the results of these commands. ## Documentation ## The full documentation for **wot** is available on Github Pages : <http://broadinstitute.github.io/wot> For more advanced usage, you may also browse the source code to read each function's documentation. Most of **wot**'s internal functions have docstrings with a description of their parameters, output and examples on how to use them. ## Developer Notes ## For more information about the internal functionning of **wot**, please refer to the [Developer Notes](developer_notes.md) [pip-install]: https://pip.pypa.io/en/stable/installing/