getColorsForGroups {SNFtool}

Obtaining a vector of colors from a numeric vector of group

Description

Convert a numeric vector containing group information to a vector of colors

Usage

getColorsForGroups(group, colors)

Arguments

group

A numeric vector containing the groups information such as the result of the spectralClustering function.

colors

a vector of colors to be used for the different groups. If the number of group is > 8, the user will have to use the colors argument and give a vector of colors with length at least equal to the number of groups.

Details

Essentially used to construct a vector or a matrix with colors used as for the ColSideColors argument in the displayClustersWithHeatmap function. See the displayClustersWithHeatmap()'s example.

Value

A character vector of colors, corresponding to the given vector of group, keeping the same order.

Author(s)

Florence Cavalli

Examples

## Example 1
gp=c(rep(1,10),rep(2,4),rep(1,3),rep(3,6))
## Using the default colors
gp_colors=getColorsForGroups(gp)
gp_colors
## Specifying the colors
gp_colors=getColorsForGroups(gp,colors=c("cyan","purple","orange"))
gp_colors

## Example 2: Part of SNF
## 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)

## 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

## Get a matrix containing the group information 
## for the samples such as the SpectralClustering result and the True label
M_label=cbind(group,truelabel)
colnames(M_label)=c("spectralClustering","TrueLabel")

## ****
## Comments
## rownames(M_label)=names(spectralClustering) To add if the spectralClustering function 
## pass the sample ID as names.
## or rownames(M_label)=rownames(W) Having W with rownames and colmanes 
## with smaple ID would help as well.
## ***

## Use the getColorsForGroups function to assign a color to each group
## NB is more than 8 groups, you will have to input a vector 
## of colors into the getColorsForGroups function
M_label_colors=t(apply(M_label,1,getColorsForGroups))
## or choose you own colors for each label, for example:
M_label_colors=cbind("spectralClustering"=getColorsForGroups(M_label[,"spectralClustering"],
colors=c("blue","green")),"TrueLabel"=getColorsForGroups(M_label[,"TrueLabel"],
colors=c("orange","cyan")))

## Visualize the clusters present in the given similarity matrix 
## as well as some sample information
## In this presentation no clustering method is ran the samples 
## are ordered in function of their group label present in the group arguments
displayClustersWithHeatmap(W, group, M_label_colors[,"spectralClustering"]) 
displayClustersWithHeatmap(W, group, M_label_colors)