/SimiC

this is the github repo for simicLASSO

Primary LanguageJupyter Notebook

SimiC

Installation

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.

Docker suppport

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

Tutorial

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.

Running the code in Python

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.

Running the code in R

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.

Evaluation of outputs

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/.