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mkatari-bioinformatics-august-2013-clustering [2014/12/11 14:55]
mkatari [K-means]
mkatari-bioinformatics-august-2013-clustering [2015/06/17 13:04]
mkatari
Line 4: Line 4:
 ====== Clustering rna-seq data ====== ====== Clustering rna-seq data ======
 continuation from [[mkatari-bioinformatics-august-2013-deseq|DESeq]] continuation from [[mkatari-bioinformatics-august-2013-deseq|DESeq]]
 +
 +[[https://drive.google.com/file/d/0B172nc4dAaaORnh3MkZqUE9PVjA/view?usp=sharing|resSig.txt]]
 +[[https://drive.google.com/file/d/0B172nc4dAaaONXFIX2YxeDRCbkE/view?usp=sharing|normalized.txt]]
 +
 +In case you didn't get DESeq to work download and load the files above
 +
 +<code>
 +resSig = read.table("resSig.txt", header=T)
 +normalized = read.table("normalized.txt", header=T, row.names=1)
 +
 +</code>
  
 Get the significant genes Get the significant genes
Line 12: Line 23:
 Get the normalized values for the significant genes Get the normalized values for the significant genes
 <code> <code>
-sigGenes.normalized = normalized[sigGenes,]+sigGenes.normalized = normalized[as.character(sigGenes),]
 </code> </code>
  
Line 70: Line 81:
  
 <code> <code>
-# this function takes an vector to be calculated.+# this function takes vector of gene expression values.
 scaleData <- function(x) { scaleData <- function(x) {
   x = as.numeric(x)   x = as.numeric(x)
Line 78: Line 89:
   return(y)   return(y)
 } }
 +</code>
  
-#we need to transpose it because apply function returns the genes as different columns.+we need to transpose it because apply function returns the genes as different columns. 
 + 
 +<code>
 scaledSigGenes = t(apply(sigGenes.normalized, 1, scaleData)) scaledSigGenes = t(apply(sigGenes.normalized, 1, scaleData))
 colnames(scaledSigGenes)=colnames(sigGenes.normalized) colnames(scaledSigGenes)=colnames(sigGenes.normalized)
 +</code>
 +
 +now to run k-means, in this case we are starting with 2 cluster.
 +
 +<code>
 +SigGenes.kmeans.2 = kmeans(scaledSigGenes, 2, nstart=25)
 +</code>
 +
 +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.
 +
 +<code>
 +SigGenes.kmeans.2$betweenss/SigGenes.kmeans.2$totss
 +</code>
 +
 +In order to determine the ideal number of k, we can try many different K's and look to see how well they performed.
 +
 +<code>
 +getBestK <- function(x) {
 +  kmeans_ss=numeric()
 +  kmeans_ss[1]=0
 +  
 +  for (i in 2:20) {
 +     kmeans_tmp=kmeans(x, i, nstart=25)
 +     #alternate way of looking at proportion of ss that is provided by between groups.
 +     #kmeans_ss[i] = kmeans_tmp$betweenss/kmeans_tmp$totss   
 +
 +     #using silhouette width to evaluate clusters.
 +     kmeans_sil= (kmeans_tmp$betweenss-kmeans_tmp$withinss)/max(kmeans_tmp$betweenss, kmeans_tmp$withinss) 
 +     kmeans_ss[i] = mean(kmeans_sil)
 +
 +
 +  }
 +  return(kmeans_ss)
 +}
 +
 +kmeans_ss=getBestK(scaledSigGenes)
 +plot(kmeans_ss)
 +
 +</code>
 +To get the genes in the different clusters
 +<code>
 +SigGenes.kmeans.2.group1 = names(which(SigGenes.kmeans.2$cluster==1))
 +SigGenes.kmeans.2.group2 = names(which(SigGenes.kmeans.2$cluster==2))
 +</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> </code>
  
Line 109: Line 198:
 <code> <code>
 pdf("heatmap.pdf") pdf("heatmap.pdf")
-heatmap.2(sigGenes.normalized, +heatmap.2(as.matrix(sigGenes.normalized)
           col=redgreen(75),           col=redgreen(75),
           hclustfun=hclust2,           hclustfun=hclust2,
mkatari-bioinformatics-august-2013-clustering.txt · Last modified: 2015/06/17 13:26 by mkatari