Kmeans inter and intra cluster ordering

Question

I am wondering what other people are doing with K-means cluster ordering. I am making heatmaps (mainly of ChIP-Seq data) and getting nice looking figures with a custom heatmap f

Answer 1

Not an exact answer to what you ask, but perhaps you might consider seriation instead of k-means clustering. It is a bit like ordination rather than clustering, but one end result is a heatmap of the seriated data which sounds similar to what you seem to be doing with k-means followed by a specifically ordered heatmap.

There is an R package for seriation, called seriation and it has a vignette, which you can get directly from CRAN

I'll answer the specifics of the Q once I've cooked up an example to try.

Ok - proper answer following on from your comment above. First some dummy data - 3 clusters of 10 samples each, on each of 3 variables.

set.seed(1)
dat <- data.frame(A = c(rnorm(10, 2), rnorm(10, -2), rnorm(10, -2)),
                  B = c(rnorm(10, 0), rnorm(10, 5), rnorm(10, -2)),
                  C = c(rnorm(10, 0), rnorm(10, 0), rnorm(10, -10)))

## randomise the rows
dat <- dat[sample(nrow(dat)),]
clus <- kmeans(scale(dat, scale = FALSE), centers = 3, iter.max = 50,
               nstart = 10)

## means of n points in each cluster
mns <- sapply(split(dat, clus$cluster), function(x) mean(unlist(x)))

## order the data by cluster with clusters ordered by `mns`, low to high
dat2 <- do.call("rbind", split(dat, clus$cluster)[order(mns)])

## heatmaps
## original first, then reordered:
layout(matrix(1:2, ncol = 2))
image(1:3, 1:30, t(data.matrix(dat)), ylab = "Observations", 
      xlab = "Variables", xaxt = "n", main = "Original")
axis(1, at = 1:3)
image(1:3, 1:30, t(data.matrix(dat2)), ylab = "Observations", 
      xlab = "Variables", xaxt = "n", main = "Reordered")
axis(1, at = 1:3)
layout(1)

Yielding: