BuildSOM {FlowSOM} | R Documentation

## Build a self-organizing map

### Description

Build a SOM based on the data contained in the FlowSOM object

### Usage

    BuildSOM(fsom, colsToUse = NULL, silent = FALSE, outlierMAD = 4, ...)

### Arguments

`fsom` | FlowSOM object containing the data, as constructed by the `ReadInput` function
`colsToUse` | Markers, channels or indices to use for building the SOM
`silent` | if `TRUE`, no progress updates will be printed
`outlierMAD` | Number of MAD when a cell is considered an outlier. See also `TestOutliers`
`...` | options to pass on to the SOM function (xdim, ydim, rlen, mst, alpha, radius, init, distf, importance)

### Value

FlowSOM object containing the SOM result, which can be used as input for the `BuildMST` function

### References

This code is strongly based on the `kohonen` package. R. Wehrens and L.M.C. Buydens, Self- and Super-organising Maps in R: the kohonen package J. Stat. Softw., 21(5), 2007

### See Also

`ReadInput`, `BuildMST`

### Examples

    # Read from file
    fileName <- system.file("extdata", "68983.fcs", package = "FlowSOM")
    flowSOM.res <- ReadInput(fileName, compensate = TRUE, transform = TRUE,
                             scale = TRUE)

    # Build the Self-Organizing Map
    # E.g. with gridsize 5x5, presenting the dataset 20 times,
    # no use of MST in neighbourhood calculations in between
    flowSOM.res <- BuildSOM(flowSOM.res, colsToUse = c(9, 12, 14:18),
                            xdim = 5, ydim = 5, rlen = 20)

    # Build the minimal spanning tree and apply metaclustering
    flowSOM.res <- BuildMST(flowSOM.res)
    metacl <- MetaClustering(flowSOM.res$map$codes,
                             "metaClustering_consensus", max = 10)

[Package FlowSOM version 2.2.0 Index]