run.pc.tsne: Run tSNE on PCA Data. Barnes-Hut implementation of...
In iCellR: Analyzing High-Throughput Single Cell Sequencing Data

Description Usage Arguments Value

This function takes an object of class iCellR and runs tSNE on PCA data. Wrapper for the C++ implementation of Barnes-Hut t-Distributed Stochastic Neighbor Embedding. t-SNE is a method for constructing a low dimensional embedding of high-dimensional data, distances or similarities. Exact t-SNE can be computed by setting theta=0.0.

run.pc.tsne(
  x = NULL,
  dims = 1:10,
  my.seed = 0,
  add.3d = TRUE,
  initial_dims = 50,
  perplexity = 30,
  theta = 0.5,
  check_duplicates = FALSE,
  pca = TRUE,
  max_iter = 1000,
  verbose = FALSE,
  is_distance = FALSE,
  Y_init = NULL,
  pca_center = TRUE,
  pca_scale = FALSE,
  stop_lying_iter = ifelse(is.null(Y_init), 250L, 0L),
  mom_switch_iter = ifelse(is.null(Y_init), 250L, 0L),
  momentum = 0.5,
  final_momentum = 0.8,
  eta = 200,
  exaggeration_factor = 12
)

`x`	An object of class iCellR.
`dims`	PC dimentions to be used for tSNE analysis.
`my.seed`	seed number, default = 0.
`add.3d`	Add 3D tSNE as well, default = TRUE.
`initial_dims`	integer; the number of dimensions that should be retained in the initial PCA step (default: 50)
`perplexity`	numeric; Perplexity parameter
`theta`	numeric; Speed/accuracy trade-off (increase for less accuracy), set to 0.0 for exact TSNE (default: 0.5)
`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)
`pca`	logical; Whether an initial PCA step should be performed (default: TRUE)
`max_iter`	integer; Number of iterations (default: 1000)
`verbose`	logical; Whether progress updates should be messageed (default: FALSE)
`is_distance`	logical; Indicate whether X is a distance matrix (experimental, default: FALSE)
`Y_init`	matrix; Initial locations of the objects. If NULL, random initialization will be used (default: NULL). Note that when using this, the initial stage with exaggerated perplexity values and a larger momentum term will be skipped.
`pca_center`	logical; Should data be centered before pca is applied? (default: TRUE)
`pca_scale`	logical; Should data be scaled before pca is applied? (default: FALSE)
`stop_lying_iter`	integer; Iteration after which the perplexities are no longer exaggerated (default: 250, except when Y_init is used, then 0)
`mom_switch_iter`	integer; Iteration after which the final momentum is used (default: 250, except when Y_init is used, then 0)
`momentum`	numeric; Momentum used in the first part of the optimization (default: 0.5)
`final_momentum`	numeric; Momentum used in the final part of the optimization (default: 0.8)
`eta`	numeric; Learning rate (default: 200.0)
`exaggeration_factor`	numeric; Exaggeration factor used to multiply the P matrix in the first part of the optimization (default: 12.0)