Skip to content
Gene regulatory network reconstruction from pseudotemporal single-cell gene expression data
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
code
data1
data2
.gitignore
.travis.yml
LICENSE
README.md
SCINGE_Example.m

README.md

Single-Cell Inference of Networks using Granger Ensembles (SCINGE)

DOI

Gene regulatory network reconstruction from pseudotemporal single-cell gene expression data. Standalone MATLAB implementation of the SCINGE algorithm. This code has been tested on MATLAB R2014b and R2018a on Linux operating systems.

Citation

If you use the SCINGE software please cite:

Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter. Network inference with Granger causality ensembles on single-cell transcriptomic data. bioRxiv 2019. doi:10.1101/534834

Dependency

This code requires the glmnet_matlab package (http://web.stanford.edu/~hastie/glmnet_matlab/download.html). Unzip glmnet_matlab.zip in either the root directory (that contains SCINGE_Example.m) or the code subdirectory.

Inputs

  • Data - Path to ordered single-cell expression data (e.g., data1/X_SCODE_data)
  • outdir - Path to folder for storing results from individual GLG Tests
  • num_replicates - Number of subsampled replicates obtained for each GLG Test
  • gene_list - Path to list of gene names corresponding to the expression data in Data (e.g., data1/tf).
  • param_list - A list of GLG hyperparameter combinations for the hyperparameters described below

GLG Hyperparameters:

  • param.ID - Identifier for GLG hyperparameter set
  • param.lambda - Sparsity parameter (lambda = 0 results in a non-sparse solution)
  • param.dT - Time resolution for GLG Test
  • param.num_lags - Number of lags for GLG Test
  • param.kernel_width - Gaussian kernel width for GLG Test
  • param.family - Distribution Family of the gene expression values (options = gaussian, poisson, default = gaussian)
  • param.prob_zero_removal - For Zero-Handling Strategy (default = 0)
  • param.prob_remove_samples - Sample removal rate for obtaining subsampled replicates (default = 0.2)
  • param.date - Valid date in the dd-mmm-yyyy or mm/dd/yyyy format.

Outputs

  • ranked_edges - Edge lists ranked according to their SCINGE scores
  • influential_genes - Genes ranked according to their SCINGE influence.

Note on Reproducibility of Results

Because the subsampling and zero-removal stages involve pseudo-random sample removals, we generate a random seed using input hyperparameters, including the date input. The results can be reproduced by providing the same inputs and date from a previous experiment.

Example

SCINGE_Example.m demonstrates a simple example with two hyperparameter sets and two replicates. It runs SCINGE on data1/X_SCODE_data and writes the results to the Output directory.

Licenses

SCINGE is available under the MIT License, Copyright © 2019 Atul Deshpande, Anthony Gitter.

The file iLasso_for_SCINGE.m has been modified from iLasso.m. The original third-party code is available under the MIT License, Copyright © 2014 USC-Melady.

You can’t perform that action at this time.