Updated README

README.md

@@ -4,99 +4,5 @@ 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/
+Read the [documentation](http://broadinstitute.github.io/wot).