Please make sure that you are using Python 3.x, and the packages in requirements.txt are properly installed. If you are using pip then you can run:
pip install -r requirements.txt
To install SimiC in Python, go to the repository folder, and run:
python setup.py install
After the Python package is installed, you need to install the R package reticulate in order to use the R API for SimiC.
If you are not able to install the package with the above installation, we also provide a Dockerfile for you to build you own docker image, and run the package within the container. For details on how to build/run python container, please see this documentation
Once the package has been successfully installed, we provide a end-to-end tutorial for analysing Clonal Kinetic data using SimiC. Please refer to the Tutorial folder for more detail.
To run SimiC with Single-cell RNA-seq of a small test example, go to folder exmaple. The test data provided here is a subsample of the hepatocypte dataset we used in our paper. The test data contains 500 cells from 3 different states.
For Python package, use the jupyter notebook SimiC-full-pipeline to genereate the GRNs and wAUC score matrices. Or you can run the scirpt in terminal:
python SimiC_exmaple.py
The default output contains 3 GRNs with 50 driver genes and 100 target genes.
To run SimiC with same settings as the python script, go the folder example/R_API/. Run the script SimiC_example.R in R or Rstudio.
After running SimiC with the test dataset you will have two outputs: incident_matrices and wAUC_matrices. To evaluate the performance of the inferred GRNs, we proposed two different metrices: Importance Dynamics and wAUA Score (see our paper for more detail). The example jupyter notebooks for them are in example/eval/.