The code in this repository accompanies the experiments performed in the paper Towards a post-clustering test for differential expression by Zhang, Kamath, and Tse.
We provide the following notebooks:
- seurat_pbmc.ipynb: R notebook for loading the PBMC dataset and clustering it with Seurat. Please see the Seurat PBMC tutorial for more information
- experiments_pbmc3k.ipynb: Python 3 notebook with TN test experiments performed on PBMC data processed by seurat_pbmc.ipynb
- experiments_synthetic_normal.ipynb: Python 3 notebook with TN test experiments performed on synthetic data
- figure_utils.py: Python 3 notebook for preparing the results from the other notebooks for presentation in the manuscript
We also provide two Python modules:
- truncated_normal.py: contains all code required to run the TN test
- figure_utils.py: contains code used for running simulations and generating plots
The TN test package can be installed via pip:
pip install truncated_normal
Import the package by adding the following line of code to your Python script:
from truncated_normal import truncated_normal as tn
For a tutorial on using the TN test module for your own projects, please refer to experiments_pbmc3k.ipynb and experiments_synthetic_normal.ipynb. We were able to install all required R and Python packages and run all of our experiments in this Docker image.