mkatari-bioinformatics-august-2013-clustering
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mkatari-bioinformatics-august-2013-clustering [2014/12/11 14:16] – mkatari | mkatari-bioinformatics-august-2013-clustering [2014/12/15 11:58] – mkatari | ||
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====== K-means ====== | ====== K-means ====== | ||
+ | The K-means method uses euclidean distance to measure distance. Since in biology we are more interested in gene expression profiles instead of magnitude of expression levels, let's scale our data so that the mean of the expression values is 0 and the expression values will be the standard deviations away from the mean. | ||
+ | |||
+ | < | ||
+ | # this function takes a vector of gene expression values. | ||
+ | scaleData <- function(x) { | ||
+ | x = as.numeric(x) | ||
+ | meanx = mean(x) | ||
+ | sdx = sd(x) | ||
+ | y = (x-meanx)/ | ||
+ | return(y) | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | we need to transpose it because apply function returns the genes as different columns. | ||
+ | |||
+ | < | ||
+ | scaledSigGenes = t(apply(sigGenes.normalized, | ||
+ | colnames(scaledSigGenes)=colnames(sigGenes.normalized) | ||
+ | </ | ||
+ | |||
+ | now to run k-means, in this case we are starting with 2 cluster. | ||
+ | |||
+ | < | ||
+ | SigGenes.kmeans.2 = kmeans(t(scaledSigGenes), | ||
+ | </ | ||
+ | |||
+ | To obtain the measure of how well the clustering has performed, we can look at the sum of squares between members of the outside group and sum of squares total. Higher the better. | ||
+ | |||
+ | < | ||
+ | SigGenes.kmeans.2$betweenss/ | ||
+ | </ | ||
+ | |||
+ | In order to determine the ideal number of k, we can try many different K's and look to see how well they performed. | ||
+ | |||
+ | < | ||
+ | getBestK <- function(x) { | ||
+ | kmeans_ss=numeric() | ||
+ | kmeans_ss[1]=0 | ||
+ | | ||
+ | for (i in 2:20) { | ||
+ | | ||
+ | | ||
+ | | ||
+ | } | ||
+ | return(kmeans_ss) | ||
+ | } | ||
+ | |||
+ | kmeans_ss=getBestK(scaledSigGenes) | ||
+ | plot(kmeans_ss) | ||
+ | |||
+ | </ | ||
+ | To get the genes in the different clusters | ||
+ | < | ||
+ | SigGenes.kmeans.2.group1 = names(which(SigGenes.kmeans.2$cluster==1)) | ||
+ | SigGenes.kmeans.2.group2 = names(which(SigGenes.kmeans.2$cluster==2)) | ||
+ | </ | ||
+ | |||
+ | |||
+ | </ | ||
mkatari-bioinformatics-august-2013-clustering.txt · Last modified: 2015/06/17 13:26 by mkatari