# affinityMatrix: Affinity matrix calculation In SNFtool: Similarity Network Fusion

## Description

Computes affinity matrix from a generic distance matrix

## Usage

 `1` ```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.

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## 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) ```

SNFtool documentation built on June 11, 2021, 9:06 a.m.