GrandPrix is a package for non-linear probabilistic dimension reduction algorithm in python, using TensorFlow and GPFlow. GrandPrix uses sparse variational approximation to project data to lower dimensional spaces. The model is described in the paper
To replicate the results in the paper please use the betaVersion branch. The master branch works with the latest version of GPflow.
N.B. The package contains several large data files which are needed to run the example notebooks. Please be sure that your system has Git Large File Storage (Git LFS) installed to download these large data files.
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 https://github.com/GPflow/GPflow.git
cd GPflow
pip install .
cd
See GPFlow page for more detailed instructions.
- Install GrandPrix package
git clone https://github.com/ManchesterBioinference/GrandPrix
cd GrandPrix
python setup.py install
cd
To run the notebooks
cd GrandPrix/notebooks
jupyter notebook
| File name |
Description |
|---|---|
| Windram | Application of GrandPrix to microarray data, models with and without informative prior. |
| McDavid | Application of GrandPrix to cell cycle data. |
| Shalek | Application of GrandPrix to single-cell RNA_seq from mouse dentritic cells. |
| Droplet_DPT | Application of GrandPrix to droplet based single-cell RNA_seq data. |
| Droplet_68K | Application of GrandPrix to ~68k PBMCs, models optimising and fixing inducing variables. |
| Guo | Application of extendend 2-D GrandPrix model to embryonic stem cells. |
| Analysing_posterior_variance | Compare posterior distributions from GrandPrix with other models. |
When running GrandPrix 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
- Create a new environment
conda create -n newEnv python=3.5
- Activate the new environment
source activate newEnv
- Create a new directory
mkdir newInstall
cd newInstall
- Follow the regular installation process described above