BranchedGP

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.

Example

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

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

Setup

This project requires Python3.7 or earlier (TensorFlow 1 requirement). Create a virtual environment, activate it and run make 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.

File name	Description
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

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

Common tasks

Tests: make test
Install dependencies (into an active virtual environment): make install
Format code: make format
Run a jupyter notebook server: make jupyter_server

elvijs / BranchedGP