SNF: Similarity Network Fusion
In IntClust: Integrated Data Analysis via Clustering

Description Usage Arguments Value Note Author(s) References See Also Examples

The function SNF performs one of the functions SNFa, SNFb or SNFc as specified by the user.

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SNF(List,type=c("data","dist","clusters"),distmeasure = c("tanimoto",
"tanimoto"),normalize=FALSE,method=NULL, NN = 20, mu = 0.5,T = 20,
clust = "agnes", linkage = "ward",alpha=0.625,StopRange=FALSE,Version="SNFa")

`List`	A list of matrices of the same type. It is assumed the rows are corresponding with the objects.
`type`	Type indicates whether the provided matrices in "List" are either data matrices, distance matrices or clustering results obtained from the data. If type="dist" the calculation of the distance matrices is skipped and if type="clusters" the single source clustering is skipped. Type should be one of "data", "dist" or"clusters".
`distmeasure`	A vector of the distance measures to be used on each data matrix. Should be of "tanimoto", "euclidean", "jaccard","hamming".
`normalize`	Logical. Indicates whether to normalize the distance matrices or not. This is recommended if different distance types are used. More details on standardization in `Normalization`.
`method`	A method of normalization. Should be one of "Quantile","Fisher-Yates", "standardize","Range" or any of the first letters of these names.
`NN`	The number of neighbours to be used in the procedure.
`mu`	The parameter epsilon. The value is recommended to be between 0.3 and 0.8.
`T`	The number of iterations.
`clust`	Choice of clustering function (character). Defaults to "agnes".
`linkage`	Choice of inter group dissimilarity (character). Defaults to "ward".
`alpha`	The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625 and is only used if the linkage is set to "flexible"
`StopRange`	Logical. Indicates whether the distance matrices with values not between zero and one should be standardized to have so. If FALSE the range normalization is performed. See `Normalization`. If TRUE, the distance matrices are not changed. This is recommended if different types of data are used such that these are comparable.
`Version`	Specifies which version of SNF to perform. Should be one of "SNFa", "SNFb" or "SNFc".

The returned value is a list with two elements:

`FusedM`	The fused similarity matrix
`DistM`	The distance matrix computed by subtracting FusedM from one
`Clust`	The resulting clustering

For now, only hierarchical clustering with the agnes function is implemented.

Marijke Van Moerbeke

WANG, B., MEZLINI, M. A., DEMIR, F., FIUME, M., TU, Z., BRUDNO, M., HAIBE-KAINS, B., GOLDENBERG, A. (2014). Similarity Network Fusion for aggregating data types on a genomic scale. Nature. 11(3) pp. 333-337.

SNFa,SNFb,SNFc

data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)

MCF7_SNFa=SNF(list(fingerprintMat,targetMat),type="data",distmeasure=c("tanimoto",
"tanimoto"),normalize=FALSE,method=NULL,NN=10,mu=0.5,T=20,clust="agnes",linkage="ward"
,alpha=0.625,StopRange=FALSE,Version="SNFa")