simule-package: Shared and Individual parts of MULtiple graphs Explicitly

In simule: A Constrained L1 Minimization Approach for Estimating Multiple Sparse Gaussian or Nonparanormal Graphical Models

Description

This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous datasets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. This package includes two graphical model options: Gaussian Graphical model and nonparanormal graphical model. The first model assumes that each dataset follows the Gaussian Distribution. The second one assumes that each dataset is nonparanormal distributed. This package provides two computational options: the multi-threading implementation and the single-threading implementation. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: <http://link.springer.com/article/10.1007/s10994-017-5635-7>.

Details

Package:	simule
Type:	Package
Version:	1.3.0
Date:	2018-07-02
License:	GPL (>= 2)

Identifying context-specific entity networks from aggregated data is an important task, often arising in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming that can be solved efficiently in parallel. In addition to Gaussian data, SIMULE can also handle multivariate nonparanormal data that greatly relaxes the normality assumption that many real-world applications do not follow. We provide a novel theoretical proof showing that SIMULE achieves a consistent result at the rate O(log(Kp)/n_tot). On multiple synthetic datasets and two biomedical datasets, SIMULE shows significant improvement over state-of-the-art multi-sGGM and single-UGM baselines.

Author(s)

Beilun Wang, Zhaoyang Wang (Arthur) Beilun Wang(Maintainer)

References

Beilun Wang, Ritambhara Singh, Yanjun Qi (2017). A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models. <http://link.springer.com/article/10.1007/s10994-017-5635-7>

Examples

## Not run: 
data(exampleData)
simule(X = exampleData , 0.05, 1, covType = "cov", TRUE)
wsimule(X = exampleData , 0.05, 1, W = matrix(1,100,100), covType = "cov", TRUE)

## End(Not run)

simule documentation built on May 1, 2019, 6:47 p.m.