Implement NetSHy network summarization via a hybrid approach (Vu et al.,) to summarize network by considering the network topology with Laplacian matrix.
Arguments
- X
An \(n\times m\) data matrix with \(m\) features and \(n\) subjects.
- A
Corresponding adjacency matrix of size \(p\) by \(p\).
- npc
Number of principal components used to summarize the network, default is set to 1.
Value
A list consists of (1) subject-level network summarization score, (2) principal component importance information: standard deviation, percent of variance explained, and cumulative proportion of variance explained, and (3) principal component feature-level loadings.
References
Vu, Thao, Elizabeth M. Litkowski, Weixuan Liu, Katherine A. Pratte, Leslie Lange, Russell P. Bowler, Farnoush Banaei-Kashani, and Katerina J. Kechris. "NetSHy: network summarization via a hybrid approach leveraging topological properties." Bioinformatics 39, no. 1 (2023): btac818.
Examples
# simulate omics data
OmicsData <- matrix(rnorm(200,0,1), nrow = 10, ncol = 20)
# simulate omics adjacency matrix
set.seed(123)
w <- rnorm(20)
w <- w/sqrt(sum(w^2))
featurelabel <- paste0('omics',1:20)
abar <- getAbar(w, FeatureLabel = featurelabel)
# extract NetSHy summarization score
netshy_score <- summarizeNetSHy(OmicsData, abar)