Repository accompanying the paper Persistent homology for high-dimensional data based on spectral methods
Compute the persistent homology of a toy dataset with compute_ph.py, of toy datasets with outliers with compute_ph_outliers.py
and that of a single-cell dataset with compute_ph_real_data.py. Changing the dataset in the top of the script allows
to compute the persistent homology of different datasets.
cd scripts
python compute_ph.py
Create the figures of the paper with the various fig_*.ipynb notebooks. The notebooks create the following figures:
- Figure 1:
fig_1.ipynb - Figure 2:
fig_ph.ipynb - Figure 3:
fig_vary_dim_mds.ipynb - Figure 4:
fig_spectral.ipynb - Figure 5:
fig_circle.ipynb - Figure 6:
fig_datasets.ipynb - Figure 7:
fig_dims.ipynb - Figure 8, 9:
fig_real_data.ipynb - Figure S1:
fig_dims.ipynb - Figure S3, S4:
fig_spectral.ipynb - Figure S5:
fig_real_data.ipynb - Figure S6, S7:
spectral.ipynb - Figure S8, S9:
fig_toy_datasets.ipynb - Figure S10:
fig_sc_datasets.ipynb - Figure S11:
fig_sensitivity.ipynb - Figure S12:
fig_outliers.ipynb - Figure S13, S14:
fig_high_dim_UMAP.ipynb - Figure S15:
fig_real_data.ipynb - Figure S16:
fig_circle.ipynb - Figure S17:
fig_datasets.ipynb - Figure S18:
fig_circle.ipynb - Figures S19-S26, S28:
fig_all_methods_on_toy.ipynb - Figure S27:
fig_torus_high_n.ipynb - Figure S29:
fig_real_data.ipynb - Figure S30:
fig_Lp.ipynb
Clone the repository
git clone https://github.com/berenslab/eff-ph.git
Create a conda python environment
cd eff-ph
conda env create -f environment.yml
Install the utils:
cd ../eff-ph
python setup.py install
Clone the repository ripser and compile it:
cd ..
git clone -b representative-cycles https://github.com/Ripser/ripser.git
cd risper
make
Clone the repository vis_utils
cd ..
git clone https://github.com/sdamrich/vis_utils.git --branch eff-ph-arxiv-v1 --single-branch
Create the conda R environment (for loading some single-cell datasets)
cd vis_utils
conda create -f r_env.yml
Install vis_utils
conda activate eff-ph
python setup.py install