runTSNE: Calculate t-Distributed Stochastic Neighbor Embedding in FSPY

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

Description

Calculate t-Distributed Stochastic Neighbor Embedding in FSPY

Usage

runTSNE(
  object,
  dims = 2,
  initial_dims = 50,
  perplexity = 30,
  theta = 0.5,
  check_duplicates = TRUE,
  pca = TRUE,
  max_iter = 1000,
  verbose = FALSE,
  is_distance = FALSE,
  Y_init = NULL,
  pca_center = TRUE,
  pca_scale = FALSE,
  ...
)

Arguments

object	an FSPY object
dims	integer, Output dimensionality (default: 2)
initial_dims	integer. the number of dimensions that should be retained in the initial PCA step (default: 50). See Rtsne
perplexity	numeric. Perplexity parameter. See Rtsne
theta	numeric. Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE (default: 0.5). See Rtsne
check_duplicates	logical. Checks whether duplicates are present. It is best to make sure there are no duplicates present and set this option to FALSE, especially for large datasets (default: TRUE). See Rtsne
pca, max_iter, is_distance, Y_init, pca_center, pca_scale	See Rtsne
verbose	logical. Whether to print calculation progress.
...	Parameters passing to Rtsne function

Value

An FSPY object

References

Maaten, L. Van Der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245.

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

See Also

Rtsne

Examples

if (FALSE) {

fspy <- runTSNE(fspy, dims = 2, verbose = TRUE)
fspy <- runTSNE(fspy, dims = 2, perplexity = 20, verbose = TRUE)

}

JhuangLab/flowSpy documentation built on July 15, 2020, 8:31 a.m.