Hi all, I'm using som() to partition samples of affy data into clusters. I rma-normalized the data, and took the means for each case (4 controls, 4 exp). It is in the form of a data frame called "all." I did not do any other filtering or scaling or norm other than a simple call to rma(). I then transposed the data frame so that its dim are 8 x 22283, and called that "allt." 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. (Also I would eventually like to cluster the genes with SOM and also like to know which genes are clustered in which grids). 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"