SNF: Similarity Network Fusion

In pfruan/abSNF: Using Association Signal Annotations to boost Similarity Network Fusion

Similarity Network Fusion

Description

Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix.

Usage

SNF(Wall, K, t)

Arguments

Wall	List of matrices. Each element of the list is a square, symmetric matrix that shows affinities of the data points from a certain view.
K	Number of neighbors in K-nearest neighbors part of the algorithm.
t	Number of iterations for the diffusion process.

Value

W is the overall status matrix derived

References

B Wang, A Mezlini, F Demir, M Fiume, T Zu, M Brudno, B Haibe-Kains, A Goldenberg (2014) Similarity Network Fusion: a fast and effective method to aggregate multiple data types on a genome wide scale. Nature Methods. Online. Jan 26, 2014
Using Association Signal Annotations to boost Similarity Network Fusion (2018), Peifeng Ruan, Ya Wang, Ronglai Shen, Shuang Wang.

Examples

#load data
data(data1)
data(data2)
data(weight1)
data(weight2)

#standard normalization of the datasets
data1 = standardNormalization(data1)
data2 = standardNormalization(data2)

# Calculate boosted distance matrices(here we calculate Euclidean Distance, 
Dist1 = dist2_w(as.matrix(data1),as.matrix(data1),weight1)
Dist2 = dist2_w(as.matrix(data2),as.matrix(data2),weight2)

# Next, construct similarity graphs
W1 = affinityMatrix(Dist1)
W2 = affinityMatrix(Dist2)

#
W = SNF(list(W1,W2), 20, 20)

pfruan/abSNF documentation built on Sept. 16, 2022, 5:40 a.m.