SNFtool (version 2.3.1)

rankFeaturesByNMI: Rank Features by NMI

Description

Ranks each features by NMI based on their clustering assingments

Usage

rankFeaturesByNMI(data, W)

Arguments

data

List containing all the data types.

W

Target Matrix for which the NMI is calculated against.

Value

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

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)

## 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)

# }