[R] cluster samples using self organizing map in R
@@r@h@go@|ee @end|ng |rom gm@||@com
Wed Oct 10 18:14:29 CEST 2018
What's wrong with what you did?
The output object of som() contains the classification of each sample.
You probably do need to read more about self-organizing maps, since
you specified you wanted the samples classified into nine groups, and
that's unlikely to be your actual intent.
I have no idea what you thought your hierarchical clustering step was
supposed to do, either.
Here's one way to get 3 groups instead of 9:
iris.sc <- scale(iris[, 1:4])
iris.som <- som(iris.sc, grid=somgrid(xdim = 1, ydim=3,
topo="rectangular"), rlen=100, alpha=c(0.05,0.01))
On Wed, Oct 10, 2018 at 2:14 AM A DNA RNA <email2mrna using gmail.com> wrote:
> Dear All,
> Who can I use Self Organizing Map (SOM) results to cluster samples? I have
> tried following but this gives me only the clustering of grids, while I
> want to cluster (150) samples:
> iris.sc <- scale(iris[, 1:4])
> iris.som <- som(iris.sc, grid=somgrid(xdim = 3, ydim=3, topo="hexagonal"),
> rlen=100, alpha=c(0.05,0.01))
> ##hierarchical clustering
> groups <- 3
> iris.hc <- cutree(hclust(dist(iris.som$codes[])), groups)
> #V1 V2 V3 V4 V5 V6 V7 V8 V9
> #1 1 2 1 1 2 3 3 2
> Can anyone help me with this please?
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