I am stuck in a problem with hierarchical clustering. I want to make a dendrogram and a heatmap, with a distance method of correlation (d_mydata=dist(1-cor(t(mydata))) and ward.D2 as clustering method.

As a gadget in the package pheatmap you can plot the dendrogram on the left side to visualize the clusters.

The pipeline of my analysis would be this:

create the dendrogram test how many cluster would be the optimal (k) extract the subjects in each cluster create a heatmap My surprise comes up when the dendrogram plotted in the heatmap is not the same as the one plotted before even when methods are the same.

So I decided to create a pheatmap colouring by the clusters classified before by cutree and test if the colours correspond to the clusters in the dendrogram.

This is my code:

Create test matrix

test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
test<-as.data.frame(test)


Create a dendrogram with this test matrix

dist_test<-dist(test)
hc=hclust(dist_test, method="ward.D2")

plot(hc)

dend<-as.dendrogram(hc, check=F, nodePar=list(cex = .000007),leaflab="none", cex.main=3, axes=F, adjust=F)

clus2 <- as.factor(cutree(hc, k=2)) # cut tree into 2 clusters
groups<-data.frame(clus2)
groups$id<-rownames(groups)

-----------DATAFRAME WITH mydata AND THE CLASSIFICATION OF CLUSTERS AS FACTORS---------------------

test$id<-rownames(test)
clusters<-merge(groups, test, by.x="id")
rownames(clusters)<-clusters$id

clusters$clus2<-as.character(clusters$clus2)
clusters$clus2[clusters$clus2== "1"]= "cluster1"
clusters$clus2[clusters$clus2=="2"]<-"cluster2"

plot(dend, 
 main = "test", 
 horiz =  TRUE, leaflab = "none")

d_clusters<-dist(1-cor(t(clusters[,7:10])))
hc_cl=hclust(d_clusters, method="ward.D2")

annotation_col = data.frame( 
  Path = factor(colnames(clusters[3:12]))
)
  rownames(annotation_col) = colnames(clusters[3:12])

annotation_row = data.frame(
    Group = factor(clusters$clus2)
)
rownames(annotation_row) = rownames(clusters)

Specify colors

ann_colors = list(
  Path= c(Test1="darkseagreen", Test2="lavenderblush2", Test3="lightcyan3", Test4="mediumpurple", Test5="red", Test6="blue", Test7="brown", Test8="pink", Test9="black", Test10="grey"), 
  Group = c(cluster1="yellow", cluster2="blue")
)

require(RColorBrewer)
library(RColorBrewer)
cols <- colorRampPalette(brewer.pal(10, "RdYlBu"))(20)
library(pheatmap)
pheatmap(clusters[ ,3:12], color =  rev(cols), 
     scale = "column",
     kmeans_k = NA,
     show_rownames = F, show_colnames = T,
     main = "Heatmap CK14, CK5/6, GATA3 and FOXA1 n=492 SCALE",
     clustering_method = "ward.D2",
     cluster_rows = TRUE, cluster_cols = TRUE,
     clustering_distance_rows = "correlation", 
     clustering_distance_cols = "correlation",
     annotation_row = annotation_row, 
     annotation_col = annotation_col, 

     annotation_colors=ann_colors
)