runTSNE {CytoTree} | R Documentation

## Calculate t-Distributed Stochastic Neighbor Embedding in CYT

### Description

Calculate t-Distributed Stochastic Neighbor Embedding in CYT

### 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` | 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

    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)

[Package CytoTree version 1.0.3 Index]