Rank Features by NMI

Description

Ranks each features by NMI based on their clustering assingments

Usage

rankFeaturesByNMI(data, W) 

Arguments

Value

List containing the NMI and rank based on NMI for each feature.

Author(s)

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

Examples

## 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));
Dist2 = dist2(as.matrix(Data2),as.matrix(Data2));

## next, construct similarity graphs
W1 = affinityMatrix(Dist1, K, alpha)
W2 = affinityMatrix(Dist2, K, alpha)

## 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)

NMI_scores <- rankFeaturesByNMI(list(Data1, Data2), W)