Description Usage Arguments Value Author(s) Examples

View source: R/rankFeaturesByNMI.R

Ranks each features by NMI based on their clustering assingments

1 |

`data` |
List containing all the data types. |

`W` |
Target Matrix for which the NMI is calculated against. |

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

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
## 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)
``` |

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