SNFtool (version 2.3.1)

estimateNumberOfClustersGivenGraph: Estimate Number Of Clusters Given Graph

Description

This function estimates the number of clusters given the two huristics given in the supplementary materials of our nature method paper W is the similarity graph NUMC is a vector which contains the possible choices of number of clusters.

Usage

estimateNumberOfClustersGivenGraph(W, NUMC=2:5)

Arguments

W

List of matrices. Each element of the list is a square, symmetric matrix that shows affinities of the data points from a certain view.

NUMC

A vector which contains the possible choices of number of clusters.

Value

K1 is the estimated best number of clusters according to eigen-gaps K12 is the estimated SECOND best number of clusters according to eigen-gaps K2 is the estimated number of clusters according to rotation cost K22 is the estimated SECOND number of clusters according to rotation cost

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

Concise description can be found here: http://compbio.cs.toronto.edu/SNF/SNF/Software.html

Examples

# NOT RUN {
## First, set all the parameters:
K = 20;  	# number of neighbors, usually (10~30)
alpha = 0.5;  	# hyperparameter, usually (0.3~0.8)
T = 20; 	# Number of Iterations, usually (10~20)

## 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)

## Here, the simulation data (SNFdata) has two data types. They are complementary to each other. 
## And two data types have the same number of points. 
## The first half data belongs to the first cluster; the rest belongs to the second cluster.
truelabel = c(matrix(1,100,1),matrix(2,100,1)); ## the ground truth of the simulated data

## Calculate distance matrices
## (here we calculate Euclidean Distance, you can use other distance, e.g,correlation)

## If the data are all continuous values, we recommend the users to perform 
## standard normalization before using SNF, 
## though it is optional depending on the data the users want to use.  
# Data1 = standardNormalization(Data1);
# Data2 = standardNormalization(Data2);



## Calculate the pair-wise distance; 
## If the data is continuous, we recommend to use the function "dist2" as follows 
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)

## These similarity graphs have complementary information about clusters.
displayClusters(W1,truelabel);
displayClusters(W2,truelabel);

## next, we fuse all the graphs
## then the overall matrix can be computed by similarity network fusion(SNF):
W = SNF(list(W1,W2), K, T)

## With this unified graph W of size n x n, 
## you can do either spectral clustering or Kernel NMF. 
## If you need help with further clustering, please let us know. 

## You can display clusters in the data by the following function
## where C is the number of clusters.
C = 2 								# number of clusters
group = spectralClustering(W,C); 	# the final subtypes information
displayClusters(W, group)

## You can get cluster labels for each data point by spectral clustering
labels = spectralClustering(W, C)

plot(Data1, col=labels, main='Data type 1')
plot(Data2, col=labels, main='Data type 2')

## Here we provide two ways to estimate the number of clusters. Note that,
## these two methods cannot guarantee the accuracy of esstimated number of
## clusters, but just to offer two insights about the datasets.

estimationResult = estimateNumberOfClustersGivenGraph(W, 2:5);
# }