Seurat (version 1.2.1)

run_tsne: Run t-distributed Stochastic Neighbor Embedding

Description

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

Usage

run_tsne(object, cells.use = NULL, dims.use = 1:5, k.seed = 1, do.fast = FALSE, add.iter = 0, genes.use = NULL, reduction.use = "pca", dim_embed = 2, ...)

Arguments

object

Seurat object

cells.use

Which cells to analyze (default, all cells)

dims.use

Which dimensions to use as input features

k.seed

Random seed for the t-SNE

do.fast

If TRUE, uses the Barnes-hut implementation, which runs faster, but is less flexible

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

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

reduction.use

Which dimensional reduction (PCA or ICA) to use for the tSNE. Default is PCA

dim_embed

The dimensional space of the resulting tSNE embedding (default is 2). For example, set to 3 for a 3d tSNE

...

Additional arguments to the tSNE call. Most commonly used is perplexity (expected number of neighbors default is 30)

Value

Returns a Seurat object with a tSNE embedding in object@tsne_rot