RunTSNE: Run t-distributed Stochastic Neighbor Embedding
In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Description Usage Arguments Value

Run t-SNE dimensionality reduction on selected features. Has the option of running in a reduced dimensional space (i.e. spectral tSNE, recommended), or running based on a set of genes. For details about stored TSNE calculation parameters, see PrintTSNEParams.

RunTSNE(object, reduction.use = "pca", cells.use = NULL, dims.use = 1:5,
  genes.use = NULL, seed.use = 1, do.fast = TRUE, add.iter = 0,
  dim.embed = 2, distance.matrix = NULL, reduction.name = "tsne",
  reduction.key = "tSNE_", ...)

`object`	Seurat object
`reduction.use`	Which dimensional reduction (e.g. PCA, ICA) to use for the tSNE. Default is PCA
`cells.use`	Which cells to analyze (default, all cells)
`dims.use`	Which dimensions to use as input features
`genes.use`	If set, run the tSNE on this subset of genes (instead of running on a set of reduced dimensions). Not set (NULL) by default
`seed.use`	Random seed for the t-SNE
`do.fast`	If TRUE, uses the Barnes-hut implementation, which runs faster, but is less flexible. TRUE by default.
`add.iter`	If an existing tSNE has already been computed, uses the current tSNE to seed the algorithm and then adds additional iterations on top of this
`dim.embed`	The dimensional space of the resulting tSNE embedding (default is 2). For example, set to 3 for a 3d tSNE
`distance.matrix`	If set, runs tSNE on the given distance matrix instead of data matrix (experimental)
`reduction.name`	dimensional reduction name, specifies the position in the object$dr list. tsne by default
`reduction.key`	dimensional reduction key, specifies the string before the number for the dimension names. tSNE_ by default
`...`	Additional arguments to the tSNE call. Most commonly used is perplexity (expected number of neighbors default is 30)