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--- mkatari-bioinformatics-august-2013-clustering [2013/08/29 09:30] – mkatari
+++ mkatari-bioinformatics-august-2013-clustering [2014/12/11 14:16] – mkatari
@@ Line 2: / Line 2: @@
-Clustering rna-seq data, continuation from [[mkatari-bioinformatics-august-2013-deseq|DESeq]]
+====== Clustering rna-seq data ======
+continuation from [[mkatari-bioinformatics-august-2013-deseq|DESeq]]
 Get the significant genes
@@ Line 35: / Line 36: @@
 <code>
 sigGenes.hclust.k2<-cutree(sigGenes.normalized.hclust, k=2)
+</code>
+Now to get all the genes that are in cluster 2 simply type.
+<code>
+hclust.k2.cluster2=names(which(sigGenes.hclust.k2==2))
+</code>
+Now we can create a new matrix/data frame with just these genes. This new matrix can be used to plot a heatmap to make it easier to see a expression profile of the cluster (see below).
+<code>
+hclust.k2.cluster2.normalized = sigGenes.normalized[hclust.k2.cluster2,]
 </code>
@@ Line 53: / Line 66: @@
 </code>
-Heatmap
+====== K-means ======
+====== Heatmap ======
 <code>
@@ Line 61: / Line 78: @@
 These functions will make it easy for us to specify how we want the clustering to be performed in the heatmap function
-</code>
+<code>
 hclust2 <- function(x, method="average", ...) {
   hclust(x, method=method, ...)
@@ Line 71: / Line 88: @@
 </code>
-Create heatmap. We can save it to a pdf file
+Create heatmap. We can save it to a pdf file. Note that sigGenes.normalized is just a matrix. Here we can provide any matrix of values, for example hclust.k2.cluster2.normalized which is the expression values of genes in cluster 2 (see above)
 <code>
@@ Line 91: / Line 108: @@
 dev.off()
 </code>