runSOM: calculation SOM in CYT object

Description Usage Arguments Value References See Also Examples

View source: R/cluster.R

Description

Build a self-organizing map

Usage

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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

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

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

CytoTree documentation built on Nov. 10, 2020, 2 a.m.