SINCERITIES

SINCERITIES is a tool for inferring gene regulatory networks from time-stamped cross-sectional single cell transcriptional expression profiles. In particular, SINCERITIES recovers the causal relationships among genes by analyzing the evolution of the distribution of gene expression levels over time, quantified using distribution distances. We formulated the GRN inference as a regularised linear regression problem with ridge regression penalty function. The MATLAB version of SINCERITIES can be found below.

System Requirements

This SINCERITIES toolbox is written for MATLAB. The subroutines in SINCERITIES (version 1.0) have been successfully tested on MATLAB 2015b and 2016a. SINCERITIES requires MATLAB statistics toolbox and three additional third-party MATLAB packages, including

1. external page glmnet_matlab 2. external page cmtest and 3. external page AnDarksamtest

These packages have been included in SINCERITIES distribution file.

SINCERITIES in R is also provided for R users. SINCERITIES-R have been successfully tested on R version 3.3.1. R packages required: kSamples, glmnet, ppcor, pracma, R.matlab.

Last Update

Current version: 1.0 (23.11.2016)

Download and Installation

SINCERITIES (MATLAB version):

Download and unzip the Download SINCERITIES.zip (ZIP, 13.4 MB) for codes and data.

SINCERITIES-R (R version):

Download and unzip the Download SINCERITIES-R.zip (ZIP, 11.8 MB) for codes and data

Supporting information for review purposes.

Download Additional File 1 (XLSX, 77 KB)

Download Additional File 2 (XLSX, 59 KB)

Download Additional File 3 (DOCX, 1.7 MB)

License

Redistribution and use in source and binary forms, with or without modification, are permitted provided agreeing to the Simplified BSD Style License.

Download License (RTF, 2 KB)

Read about Simplified BSD Style License at external page http://www.opensource.org/licenses/bsd-license.php

References

Papili Gao N., Ud-Dean S.M.M. and Gunawan R., Inferring gene regulatory networks from time- stamped single cell transcriptional expression profiles. external page abstract (available on biorxiv)

Acknowledgement

This work is supported by funding from Swiss National Science Foundation.