SNFtool (version 2.3.1)

affinityMatrix: Affinity matrix calculation

Description

Computes affinity matrix from a generic distance matrix

Usage

affinityMatrix(diff, K = 20, sigma = 0.5)

Arguments

diff

Distance matrix

K

Number of nearest neighbors

sigma

Variance for local model

Value

Returns an affinity matrix that represents the neighborhood graph of the data points.

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

Examples

# NOT RUN {
## First, set all the parameters:
K = 20; ##number of neighbors, must be greater than 1. usually (10~30)
alpha = 0.5; ##hyperparameter, usually (0.3~0.8)
T = 20; ###Number of Iterations, usually (10~50)

## Data1 is of size n x d_1, 
## where n is the number of patients, d_1 is the number of genes, 
## Data2 is of size n x d_2, 
## where n is the number of patients, d_2 is the number of methylation
data(Data1)
data(Data2)

## Calculate distance matrices(here we calculate Euclidean Distance, 
## you can use other distance, e.g. correlation)
Dist1 = (dist2(as.matrix(Data1),as.matrix(Data1)))^(1/2)
Dist2 = (dist2(as.matrix(Data2),as.matrix(Data2)))^(1/2)

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

# }