runTSNE: Calculate t-Distributed Stochastic Neighbor Embedding in CYT

Description Usage Arguments Value References See Also Examples

View source: R/tsne.R

Description

Calculate t-Distributed Stochastic Neighbor Embedding in CYT

Usage

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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

a CYT 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

A CYT 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

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

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

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