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Branching Gaussian process
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BranchedGP is a package for building Branching Gaussian process models in python, using TensorFlow and GPFlow. The model is described in the paper

"BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data", Alexis Boukouvalas, James Hensman, Magnus Rattray, bioRxiv, 2017..

This is now published in Genome Biology. Build Status codecov


An example of what the model can provide is shown below.

  1. The posterior cell assignment is shown in top subpanel: each cell is assigned a probability of belonging to a branch.
  2. In the bottom subpanel the posterior branching time is shown: the probability of branching at a particular pseudotime.


If you have any problems with installation see the script at the bottom of the page for a detailed setup guide from a new python environment.

  • Install tensorflow
pip install tensorflow
  • Install GPflow
git clone
cd GPflow    
pip install .

See GPFlow page for more detailed instructions.

  • Install Branched GP package
git clone
cd BranchedGP
python install

Quick start

For a quick introduction see the notebooks/Hematopoiesis.ipynb notebook. Therein we demonstrate how to fit the model and compute the log Bayes factor for two genes.

The Bayes factor in particular is calculated by calling CalculateBranchingEvidence after fitting the model using FitModel.

This notebook should take a total of 6 minutes to run.


To run the tests should takes < 3min.

pip install nose
pip install nose-timer
cd BranchedGP/testing
nosetests --pdb-failures --pdb --with-timer

List of notebooks

To run the notebooks

cd BranchedGP/notebooks
jupyter notebook
Hematopoiesis Application of BGP to hematopoiesis data.
SyntheticData Application of BGP to synthetic data.
SamplingFromTheModel Sampling from the BGP model.

Comparison to monocle-BEAM

In the paper we compare the BGP model to the BEAM method proposed in monocle 2. In monocle/runMonocle.R the R script for performing Monocle and BEAM on the hematopoiesis data is included.

List of python library files

Description Main file for user to call BGP fit, see function FitModel Construct prior on assignments; use by variational code. Variational inference code to infer function labels. Sparse inducing point variational inference code to infer function labels. Branching kernels. Includes independent kernel as used in the overlapping mixture of GPs and a hardcoded branch kernel for testing. Code to generate branching tree. Plotting code.

Running in a cluster

When running BranchingGP in a cluster it may be useful to constrain the number of cores used. To do this insert this code at the beginning of your script.

from gpflow import settings
settings.session.intra_op_parallelism_threads = NUMCORES
settings.session.inter_op_parallelism_threads = NUMCORES
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