Differences

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--- mkatari-bioinformatics-august-2013-clustering [2014/12/15 12:01] – mkatari
+++ mkatari-bioinformatics-august-2013-clustering [2014/12/15 12:45] – mkatari
@@ Line 35: / Line 35: @@
 <code>
-sigGenes.hclust.k2<-cutree(sigGenes.normalized.hclust, k=2, nstart=25)
+sigGenes.hclust.k2<-cutree(sigGenes.normalized.hclust, k=2)
 </code>
@@ Line 90: / Line 90: @@
 <code>
-SigGenes.kmeans.2 = kmeans(t(scaledSigGenes), 2)
+SigGenes.kmeans.2 = kmeans(t(scaledSigGenes), 2, nstart=25)
 </code>
@@ Line 107: / Line 107: @@
   for (i in 2:20) {
-     kmeans_tmp=kmeans(x, 2, nstart=25)
+     kmeans_tmp=kmeans(x, i, nstart=25)
      kmeans_ss[i] = kmeans_tmp$betweenss/kmeans_tmp$totss
@@ Line 125: / Line 125: @@
+</code>
+The code below plots k-means clustering results. You simply have to provide the k-means output and the labels.
+<code>
+plotClusterCenters<-function(kmeansres,
+                             myxlab="Treatment",
+                             myylab="Expression",
+                             mymain="K-means Clusters") {
+  mycolors=c("blue","red","green","orange","pink","black")
+  centersdim = dim(kmeansres$centers)
+  plot(kmeansres$centers[1,],
+       type="b",
+       col=mycolors[1],
+       xlab=myxlab,
+       ylab=myylab,
+       main=mymain,
+       ylim=c(round(min(kmeansres$centers)),
+                               round(max(kmeansres$centers))),
+                               xaxt="n")
+  axis(1, at=c(1:centersdim[2]), labels=names(kmeansres$centers[1,]))
+  for (i in 2:centersdim[1]) {
+    lines(kmeansres$centers[i,], type="b", col=mycolors[i])
+  }
+}
+plotClusterCenters(SigGenes.kmeans.2)
 </code>