[R] cluster samples using self organizing map in R

[Sarah Goslee]

Hi Tina,

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:

library(kohonen)
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))

table(iris.som$unit.classif, iris$Species)
plot(iris.som)

Sarah

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:
>
> library(kohonen)
> 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[[1]])), groups)
> iris.hc
> #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?
> --
> Tina


-- 
Sarah Goslee
http://www.functionaldiversity.org