SNF | R Documentation |

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

SNF(Wall, K, t)

`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. |

W is the overall status matrix derived

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.

#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)

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