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# runFlowSOM

Source: `R/clustering.R`
`runFlowSOM.Rd`

Run FlowSOM based clustering.

## Usage

    runFlowSOM(
      fcd,
      input_type,
      data_slot,
      nClusters,
      grid_xdim = 10,
      grid_ydim = 10,
      seed = 91,
      prefix = NULL,
      ret_model = FALSE,
      nPC = ncol(fcd[[input_type]][[data_slot]]),
      markers = colnames(fcd$expr[[data_slot]]),
      discard = FALSE
    )

## Arguments

fcd

  flow cytometry dataset.

input_type

  data to use for the calculation, e.g. "expr" or "pca".

data_slot

  data slot to use for the calculation, e.g. "orig" or "norm".

nClusters

  Number of final clusters.

grid_xdim

  x-axis size of the FlowSOM grid. Default = 10.

grid_ydim

  y-axis size of the FlowSOM grid. Default = 10.

seed

  A seed is set for reproducibility.

prefix

  Optional prefix for the slot name of the output.

ret_model

  LOGICAL if the model should be saved for future projection of the data. Default = F.

nPC

  Number of principal components to use for the analysis. Default = All.

markers

  vector of marker names to include or exclude from the calculation according to the discard parameter. See functions used_markers and measured_markers for the extraction of markers directly from the condor object

discard

  LOGICAL if the markers specified should be included, "F", or excluded, "T", from the calculation. Default = F.

## Value

The function returns a fcd including a data frame containing the FlowSOM clustering saved in `fcd$clustering`. The name of the output consists of the prefix (if given) and the data slot and the defined `nClusters`. If a `nPC` is given it will be added to the output name.

## Details

runFlowSOM

See [Van Gassen S et al. (2015) "FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data." Cytom Part J Int Soc Anal Cytol 87: 636-645.] (https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.22625)