Hi,
Try allt.som$visual
Edgar Acuna
UPRM
On Thu, 9 Jun 2005, Ken Termiso wrote:
> Hi all,
>
> I originally posted this to the bioconductor group, but maybe it's
better
> suited to the r-help...
>
> I'm using som() to partition samples of gene expression data into
clusters.
> The point is to classify control vs. experimental cases (sample
clustering).
> The original matrix was 22283 x 8. The 8 samples have 4 controls and 4
> experimentals.
>
> I transposed the matrix so that its dim are 8 x 22283, and called that
> "allt." Using the normalize() function from som library, I scaled
the data
> to have mean zero and variance 1.
>
> allt.som <- som(allt, xdim=5, ydim=5, topol="hexa",
neigh="bubble", alpha=1)
> plot(allt.som)
>
> What I cannot figure out how to do is how to determine where each sample
has
> clustered, since the plot that i'm using does not include labels...I
also
> tried str(allt.som) but cannot determine which attribute calls where each
> sample has gone...all I would like to know is where samples are being
placed
> in the SOM grids, to make sure that the controls cluster together and exps
> cluster together. (Also I would eventually like to cluster the genes with
> SOM and also like to know which genes are clustered in which grids, which
is
> the same problem as I have with the samples).
>
> Thanks in advance,
> Ken
>
>
> >str(allt.som)
> List of 16
> $ data : num [1:8, 1:22277] 1167 1282 1561 1398 1581 ...
> ..- attr(*, "dimnames")=List of 2
> .. ..$ : chr [1:8] "m577con" "m577exp"
"m578con" "m578exp" ...
> .. ..$ : chr [1:22277] "1007_s_at" "1053_at"
"117_at" "121_at" ...
> $ code : matrix [1:25, 1:22277] 1050 1222 1411 1504 1722 ...
> ..- attr(*, "class")= chr "matrix"
> $ visual :`data.frame': 8 obs. of 3 variables:
> ..$ x : num [1:8] 0 1 3 2 3 4 2 1
> ..$ y : num [1:8] 1 2 0 1 3 3 2 4
> ..$ qerror: num [1:8] 6472 8396 7574 7856 6969 ...
> $ qerror : num 6e+08
> $ init : chr "linear"
> $ alpha : chr "inverse"
> $ neigh : chr "bubble"
> $ topol : chr "hexa"
> $ alpha0 : num [1:2] 1 0.5
> $ radius0 : num [1:2] 5 3
> $ rlen : num [1:2] 16 80
> $ xdim : num 5
> $ ydim : num 5
> $ err.radius: num 1
> $ inv.alp.c : num [1:2] 0.16 0.8
> $ code.sum :`data.frame': 25 obs. of 3 variables:
> ..$ x : num [1:25] 0 1 2 3 4 0 1 2 3 4 ...
> ..$ y : num [1:25] 0 0 0 0 0 1 1 1 1 1 ...
> ..$ nobs: int [1:25] 0 0 0 1 0 1 0 1 0 0 ...
> - attr(*, "class")= chr "som"
>
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