R/jeek-package.R

In jeek: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

#' A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in
#' Learning Multiple Related Sparse Gaussian Graphical Models
#'
#' This is an R implementation of a Fast and Scalable Joint Estimator for
#' Integrating Additional Knowledge in Learning Multiple Related Sparse
#' Gaussian Graphical Models (JEEK).The JEEK algorithm can be used to fast
#' estimate multiple related precision matrices in a large-scale. For instance,
#' it can identify multiple gene networks from multi-context gene expression
#' datasets. By performing data-driven network inference from high-dimensional
#' and heterogenous data sets, this tool can help users effectively translate
#' aggregated data into knowledge that take the form of graphs among entities.
#' Please run demo(jeek) to learn the basic functions provided by this package.
#' For further details, please read the original paper: Beilun Wang, Arshdeep
#' Sekhon, Yanjun Qi (2018).
#'
#' \tabular{ll}{ Package: \tab jeek\cr Type: \tab Package\cr Version: \tab
#' 1.1.0\cr Date: \tab 2018-07-03\cr License: \tab GPL (>= 2)\cr } We consider
#' the problem of including additional knowledge in estimating sparse Gaussian
#' graphical models (sGGMs) from aggregated samples, arising often in
#' bioinformatics and neuroimaging applications. Previous joint sGGM estimators
#' either fail to use existing knowledge or cannot scale-up to many tasks
#' (large \eqn{K}) under a high-dimensional (large \eqn{p}) situation.  In this
#' paper, we propose a novel Joint Elementary Estimator incorporating
#' additional Knowledge (JEEK) to infer multiple related sparse Gaussian
#' Graphical models from large-scale heterogeneous data. Using domain knowledge
#' as weights, we design a novel hybrid norm as the minimization objective to
#' enforce the superposition of two weighted sparsity constraints, one on the
#' shared interactions and the other on the task-specific structural patterns.
#' This enables JEEK to elegantly consider various forms of existing knowledge
#' based on the domain at hand and avoid the need to design knowledge-specific
#' optimization. JEEK is solved through a fast and entry-wise parallelizable
#' solution that largely improves the computational efficiency of the
#' state-of-the-art \eqn{O(p^5K^4)} to \eqn{O(p^2K^4)}. We conduct a rigorous
#' statistical analysis showing that JEEK achieves the same convergence rate
#' \eqn{O(\log(Kp)/n_{tot})} as the state-of-the-art estimators that are much
#' harder to compute. Empirically, on multiple synthetic datasets and one
#' real-world data from neuroscience, JEEK outperforms the speed of the
#' state-of-arts significantly while achieving the same level of prediction
#' accuracy.
#'
#' @name jeek-package
#' @docType package
#' @author Beilun Wang, Zhaoyang Wang
#'
#' Maintainer: Beilun Wang - bw4mw at virginia dot edu
#' @references Beilun Wang, Arshdeep Sekhon, Yanjun Qi. A Fast and Scalable
#' Joint Estimator for Integrating Additional Knowledge in Learning Multiple
#' Related Sparse Gaussian Graphical Models. <arXiv:1806.00548>
#' @keywords package
#' @examples
#' \dontrun{
#' data(exampleData)
#' result = jeek(X = exampleData, 0.3, covType = "cov", parallel = TRUE)
#' plot.jeek(results)
#' }
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