runSOM: calculation SOM in CYT object

In JhuangLab/CytoTree: A Toolkit for Flow And Mass Cytometry Data

Description

Build a self-organizing map

Usage

runSOM(
  object,
  xdim = 6,
  ydim = 6,
  rlen = 8,
  mst = 1,
  alpha = c(0.05, 0.01),
  radius = 1,
  init = FALSE,
  distf = 2,
  codes = NULL,
  importance = NULL,
  method = "euclidean",
  verbose = FALSE,
  ...
)

Arguments

object	a CYT object
xdim	Width of the grid.
ydim	Hight of the grid.
rlen	Number of times to loop over the training data for each MST
mst	Number of times to build an MST
alpha	Start and end learning rate
radius	Start and end radius
init	Initialize cluster centers in a non-random way
distf	Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
codes	Cluster centers to start with
importance	array with numeric values. Parameters will be scaled according to importance
method	the distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given. See dist
verbose	logical. Whether to print calculation progress.
...	Parameters passing to SOM function

Value

a CYT object with som.id in CYT object

References

This code is strongly based on the SOM function. Which is developed by Sofie Van Gassen, Britt Callebaut and Yvan Saeys (2018).

See Also

BuildSOM

SOM

Examples

cyt.file <- system.file("extdata/cyt.rds", package = "CytoTree")
cyt <- readRDS(file = cyt.file)

cyt <- runSOM(cyt, xdim = 10, ydim = 10, verbose = TRUE)

JhuangLab/CytoTree documentation built on Nov. 16, 2020, 7:23 a.m.