README.md

In broadinstitute/CommunityAMARETTO: CommunityAMARETTO: A Computational Tool for the Discovery of Shared and Distinct Regulatory Mechanisms Across Biological Systems

The goal of the CommunityAMARETTO algorithm is to identify cell circuits and their drivers that are shared and distinct across biological systems. Specifically, Community-AMARETTO takes as input multiple regulatory networks inferred using the AMARETTO algorithm that are based on multi-omics data fusion. Next, Community-AMARETTO learns communities or subnetworks, in particular, regulatory modules comprising of cell circuits and their drivers, that are shared and distinct across multiple regulatory networks derived from multiple cohorts, diseases, or biological systems more generally, using the Girvan-Newman "edge betweenness community detection" algorithm (Girvan and Newman, Physical Review E. 2004).

Table of Contents

• Introduction
• Installation
• Running Community-AMARETTO
• References

Introduction

Many researchers have long sought to uncover gene regulatory mechanisms underlying diseases and cancer. This interest has led to the development of many novel computational algorithms for regulatory network inference using multiomics such as genetics, epigenetics and transcriptomics.

We developed Community-AMARETTO to integrate multiple regulatory networks inferred by the AMARETTO algorithm [1] across multiple systems to highlight key information about cross-systems shared and distinct mechanisms. More specifically, Community-AMARETTO algorithm consists of 1) constructing a master network composed of multiple regulatory networks followed by 2) detecting groups (communities) of circuits that are shared across systems as well as highliting circuits that are system-specific and distinct.

Installation

Install from the GitHub repository using devtools:

library(devtools)
install_github("broadinstitute/CommunityAMARETTO")

Running Community-AMARETTO

• The vignettes contains an example R script for a typical AMARETTO analysis. Please try!

• Detailed information on CommunityAMARETTO package functions can be obtained in the help files. For example, to view the help file for the function CommunityAMARETTO in a R session, use ?CommunityAMARETTO.

References

1. Gevaert, O., Villalobos, V., Sikic, B. I. & Plevritis, S. K. Identification of ovarian cancer driver genes by using module network integration of multi-omics data. Interface Focus 3, 20130013–20130013 (2013).
2. Gevaert, O. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics 31, 1839–1841 (2015).
3. AMARETTO package in Bioconductor.

Useful Links

GitHub: AMARETTO: https://github.com/gevaertlab/AMARETTO Community-AMARETTO: https://github.com/broadinstitute/CommunityAMARETTO

GenePattern: AMARETTO: https://beta.genepattern.org/gp/pages/index.jsf?lsid=urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00378:0.52 Community-AMARETTO: https://beta.genepattern.org/gp/pages/index.jsf?lsid=urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00380:999999999

broadinstitute/CommunityAMARETTO documentation built on April 6, 2020, 10:44 p.m.