reproduce k-means clustering and heatmap

I asked this question in seqanswer and biostar
http://seqanswers.com/forums/showthread.php?p=115844#post115844

I was plotting a ChIP-seq data using the pheatmap, see code below:

km<- kmeans(m1,2) # determine how many cluster you want, I specify 2 here

m.kmeans<- cbind(m1, km$cluster) # combine the cluster with the matrix

dim(m.kmeans)
# [1] 903 602
# the last column is 602
o<- order(m.kmeans[,602]) # order the last column

m.kmeans<- m.kmeans[o,] # order the matrix according to the order of the last column

pheatmap( m.kmeans[,1:601], cluster_rows = F, cluster_cols = F, col= hmcols, breaks = bk, legend=FALSE, show_rownames=FALSE, show_colnames=FALSE)

It works fine for me, I clustered the data to two groups by specifying K=2, the problem is that group 1 sometimes shows up in the upper part of the heatmap, sometimes it shows up in the bottom part of the figure if I plot it several times.

I think it has to do with the assignment of the group number, say, the first group is assigned to 1, the other is assigned to 2. However, next time if you plot the same data, the first group assigned to 2, the other is assigned to 1. R randomly assigns the number to the groups.

How can I  control this?

The answer is to use the set.seed() function.
http://stackoverflow.com/questions/7501035/k-means-same-clusters-for-every-execution/7501152#7501152

http://stackoverflow.com/questions/13605271/reasons-for-using-the-set-seed-function

http://stackoverflow.com/questions/18215638/argument-of-set-seed-in-r

"Yes, calling set.seed(foo) immediately prior to running kmeans(....) will give the same random start and hence the same clustering each time. foo is a seed, like 42 or some other numeric value."

"The seed argument in set.seed is a single value, interpreted as an integer (as defined inhelp(set.seed()). The seed in set.seed produces random values which are unique to thatseed (and will be same irrespective of the computer you run and hence ensures reproducibility). So the random values generated by set.seed(1) and set.seed(123) will not be the same but the random values generated by R in your computer using set.seed(1) and by R in my computer using the same seed are the same."


"For a normal RNG of decent quality, the value doesn't matter. "42" is a reference to a famous book; other people use their birthday or "123" or just "1"."