Description Usage Arguments Value Author(s) Examples

View source: R/displayClusters.R

Visualize the clusters in given similarity matrix

1 | ```
displayClusters(W, group)
``` |

`W` |
Similarity matrix |

`group` |
A vector containing the labels for each sample in W. |

Plots given similarity matrix with patients ordered to form clusters.

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 | ```
## First, set all the parameters:
K = 20; # number of neighbors, usually (10~30)
alpha = 0.5; # hyperparameter, usually (0.3~0.8)
T = 10; # 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);
``` |

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