KDiffNet

This repository contains code for estimating Differential Networks using extra evidence as described in the following paper:

Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
Arshdeep Sekhon, Zhe Wang, Yanjun Qi International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Estimating Differential Matrix using KDiffNet

To estimate the differential matrix estimated_delta given two sets of samples, X_c and X_d and edge level known knowledge W_E and node group knowledge G_V:

sourceDirectory("kdiffnet/")
estimated_delta = kdiffnet(X_c, X_d, W_E, G_V, epsilon, lambda)

Here, lambda and epsilon are hyperparameters.

Generating Simulated Data

To generate a ground truth differential matrix truth_delta, we first need to specify its generating parameters. To generate truth_delta from the edge level matrix W_E and known groups G_V, with a sparsity level of delta_sparsity and individual_sparsity.

graphs = simulate_w_group(W, g, delta_sparsity, individual_sparsity)
Omega_d = graphs[[2]]
Omega_c = graphs[[1]]
truth_delta=(Omega_d - Omega_c)

We can then generate two sets of samples, X_c and X_d, each following a Gaussian Distribution with inverse covariance matrix as Omega_candOmega_d ` respectively:

f <- function(m) class(try(solve(m),silent=T))=="matrix"
library(Hmisc)
library(R.utils)
library(boot)
library(MASS)
covcTrue =  solve(Omega_c)
covdTrue =  solve(Omega_d)
X_c = mvrnorm(nc, mu = rep(0,nrow(W_E)), covcTrue)
X_d = mvrnorm(nd, mu = rep(0,nrow(W_E)), covdTrue)

Here, nc and nd are the number of samples we wish to generate.