Description Usage Arguments Value Author(s) References Examples

View source: R/affinityMatrix.R

Computes affinity matrix from a generic distance matrix

1 | ```
affinityMatrix(diff, K = 20, sigma = 0.5)
``` |

`diff` |
Distance matrix |

`K` |
Number of nearest neighbors |

`sigma` |
Variance for local model |

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

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

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

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)
``` |

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