Affinity matrix calculation

Description

Computes affinity matrix from a generic distance matrix

Usage

affinityMatrix(Diff, K, sigma)

Arguments

Value

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

Author(s)

Dr. Anna Goldenberg, Bo Wang, Aziz Mezlini, Feyyaz Demir

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

## 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))
Dist2 = dist2(as.matrix(Data2),as.matrix(Data2))

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