Seurat (version 3.0.1)

RunTSNE: 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. For details about stored TSNE calculation parameters, see PrintTSNEParams.

Usage

RunTSNE(object, ...)
# S3 method for matrix
RunTSNE(object, assay = NULL, seed.use = 1,
  tsne.method = "Rtsne", add.iter = 0, dim.embed = 2,
  reduction.key = "tSNE_", ...)
# S3 method for DimReduc
RunTSNE(object, cells = NULL, dims = 1:5,
  seed.use = 1, tsne.method = "Rtsne", add.iter = 0, dim.embed = 2,
  reduction.key = "tSNE_", ...)
# S3 method for dist
RunTSNE(object, assay = NULL, seed.use = 1,
  tsne.method = "Rtsne", add.iter = 0, dim.embed = 2,
  reduction.key = "tSNE_", ...)
# S3 method for Seurat
RunTSNE(object, reduction = "pca", cells = NULL,
  dims = 1:5, features = NULL, seed.use = 1, tsne.method = "Rtsne",
  add.iter = 0, dim.embed = 2, distance.matrix = NULL,
  reduction.name = "tsne", reduction.key = "tSNE_", ...)

Arguments

object

Seurat object

...

Arguments passed to other methods and to t-SNE call (most commonly used is perplexity)

assay

Name of assay that that t-SNE is being run on

seed.use

Random seed for the t-SNE

tsne.method

Select the method to use to compute the tSNE. Available methods are:

• Rtsne: Use the Rtsne package Barnes-Hut implementation of tSNE (default)

• FIt-SNE: Use the FFT-accelerated Interpolation-based t-SNE. Based on Kluger Lab code found here: https://github.com/KlugerLab/FIt-SNE

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

reduction.key

dimensional reduction key, specifies the string before the number for the dimension names. tSNE_ by default

cells

Which cells to analyze (default, all cells)

dims

Which dimensions to use as input features

reduction

Which dimensional reduction (e.g. PCA, ICA) to use for the tSNE. Default is PCA

features

If set, run the tSNE on this subset of features (instead of running on a set of reduced dimensions). Not set (NULL) by default; dims must be NULL to run on features

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