[cleanup] all files and update readme

README.md

@@ -1,8 +1,4 @@
-# topslam
-
-[![Build Status](https://travis-ci.org/mzwiessele/manifold.svg?branch=master)](https://travis-ci.org/mzwiessele/manifold) [![codecov.io](http://codecov.io/github/mzwiessele/manifold/coverage.svg?branch=master)](http://codecov.io/github/mzwiessele/manifold?branch=master)
-
-Extracting and using probabilistic Waddington's landscape recreation from single cell gene expression measurements.
+# topslam frozen state for publication
 
 ## Citation
 
@@ -24,44 +20,4 @@ Extracting and using probabilistic Waddington's landscape recreation from single
     }
 
 
-## Installation
-
-*Not yet uploaded, publication pending*
-
-`$ pip install topslam`
-
-## Data filtering and usage
-
-It is recommended to use pandas to hold your data.
-
-RNAseq experiments suffer from a lot of different sources of noise. Therefore,
-we try to filter very harshly to make sure we capture as much signal as possible.
-This can also be detrimental to the optimization process and you might
-choose to filter differently. All of filtering is strongly dependent on your
-data. Thus, know your data and decide whether to use this filtering or
-filter yourself for the optimal signal to noise ratio.
-
-This is (empirically in our experience) the optimal way (we found) of using a
-selected subset of genes in order to learn a BayesianGPLVM for it.
-
-## Model Opimization
-
-We provide an optimization routine in `topslam.optimize.optimize_model`, which optimizes a model in the way we optimize the models.
-This is only a first order help for optimization. If you have trouble optimizing your data, consider z-normalization before optimization.
-Additionally we provide a model creator, which creates a BayesianGPLVM model for you. See the example application below for usage.
-
-Remarks for an optimized model for standard normalized data (zero mean and unit variance) after optimization:
-
-  - Gaussian_noise.variance smaller then 0.5
-  - rbf.variance value between 0 and 1, but bigger then Gaussian_noise.variance
-  - dimensionality of landscape can be seen by m.kern.plot_ARD(), big values are used dimensions.
-  - plot_latent does not show Waddington's landscape (as grayscale), but the certainty of the model.
-  - plot_magnification shows a representation of Waddington's landscape (as grayscale), although we use a transformation of that to show valleys more finegrained.
-
-## Example Application
-
-```python
-
-
-
-```
+The code used for the publication is frozen in the code.zip file of this branch.