Low-Rank Decomposition of Brain Connectivity Matrices with Uniform Sparsity.

R/LOCUS

Low-rank decomposition of brain connectivity matrices with universal sparsity

Author:Yikai Wang, Jialu Ran, Ying Guo

Description

LOCUS is a blind source separation (BSS) method for decomposing symmetric matrices such as brain connectivity matrices to extract sparse latent component matrices and also estimate mixing coefficients. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings. The LOCUS method was published in Wang and Guo (2023).

Usage

Below is an illustration of the the main function on simulated data.

## Simulated the data to use
V = 50
S1 = S2 = S3 = matrix(0,ncol = V,nrow = V)
S1[5:20,5:20] = 4;S1[23:37,23:37] = 3;S1[40:48,40:48] = 3
S2[15:20,] = -3;S2[,15:20] = -3
S3[15:25,36:45] = 3; S3[36:45,15:25] = 3
Struth = rbind(Ltrans(S1,FALSE) , Ltrans(S2,FALSE), Ltrans(S3,FALSE))
set.seed(100)
Atruth = matrix(rnorm(100*3),nrow=100,ncol=3)
Residual = matrix(rnorm(100*dim(Struth)[2]),nrow=100)
Yraw = Atruth%*%Struth + Residual

## Run Locus on the data 
Locus_result = LOCUS(Yraw,3,V)

## Visualize the result
par(mfrow=c(2,3))
for(i in 1:dim(Struth)[1]){image(Ltrinv(Struth[i,],V,FALSE))}
for(i in 1:dim(Locus_result$S)[1]){image(Ltrinv(Locus_result$S[i,],V,FALSE))}

References