R hierarchical clustering visualizing classifications without clustering on them - r

Question

I've been studying some data sets using hierarchical clustering. In these data sets, there are a certain number of variables that I want to use to cluster data, and then there are other classification variables that I do not want to cluster on but still want to visualize.
What I want to do is find a way to "add a tier" to the heat map generated by the clustering algorithm where I can view binary classifications (colored red for 1, blue for 0), without actually clustering on this data. That way, I can evaluate how well my classification responses are grouped together by clustering.
Here is a simplified example:
library("gplots")
set.seed(1)
## creating random data to input into hierarchial clustering algorithm
data <- matrix(rexp(100, rate = 0.1), ncol = 10)
colnames(data) <- c("var1", "var2", "var3", "var4", "var5", "var6",
"var7", "var8", "var9", "var10")
# these are the two classification labels for each data point
classification1 <- c(1, 1, 0, 1, 1, 0, 0, 0, 1, 1)
# I want to visualize how well the clustering algorithm groups
# the data correlates with the classifications without
# clustering on these classifications
classification2 <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 0)
par(mar = c(1, 4.5, 0.1, 0.1))
matrix = rbind(c(1, 2), c(3, 4), c(5, 6))
wid = c(1, 1)
hei = c(0.5, 10)
hclustfunc <- function(x) hclust(x, method = "complete")
distfunc <- function(x) dist(x, method = "euclidean")
my_palette <- colorRampPalette(c("yellow", "orange", "darkorange",
"red", "darkred"))(n = 1000)
heatmap.2(as.matrix(data), dendrogram = "row", trace = "none",
margin = c(8, 9), hclust = hclustfunc, distfun = distfunc,
col = my_palette, key = FALSE, key.xlab = "", key.title = "Clustering Algorithm",
key.ylab = "", keysize = 1.25, density.info = "density",
lhei = hei)
This generates the heat map that has given me a lot of information. What I would now like to do is append two more columns to the right of the heat map that the clustering algorithm does not use for clustering.
These two columns would be binary labels for "classification 1" and "classification 2" (and have a red cell for 1, a blue cell for 0). I just want a visualization of how well these classification responses are grouped together in the dendogram.

Answer 1

If you have only one classification to add you can just use heatmap.2 with the RowSideColors options. But if you have multiple classifications to add you'll use heatmap.plus. The options are slightly different than for heatmap and heatmap.2, but the important one for your question is that RowSideColors option takes a matrix.
library(heatmap.plus)
class1_cols <- c('red', 'blue')[classification1+1]
class2_cols <- c('red','blue')[classification2+1]
anno <- data.frame(class1 = class1_cols, clas2 = class2_cols)
heatmap.plus(as.matrix(data), col = my_palette,
RowSideColors = as.matrix(anno))