Seurat (version 2.3.4)

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, reduction.use = "pca", cells.use = NULL, dims.use = 1:5,
  genes.use = 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

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

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)

• tsne: standard tsne - not recommended for large datasets

• 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

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)

Value

Returns a Seurat object with a tSNE embedding in object@dr$[e-mail]

Examples

# NOT RUN {
pbmc_small
# Run tSNE on first five PCs, note that for test dataset (only 80 cells)
# we can't use default perplexity of 30
pbmc_small <- RunTSNE(pbmc_small, reduction.use = "pca", dims.use = 1:5, perplexity=10)
# Run tSNE on first five independent components from ICA
pbmc_small <- RunICA(pbmc_small,ics.compute=5)
pbmc_small <- RunTSNE(pbmc_small, reduction.use = "ica", dims.use = 1:5, perplexity=10)
# Plot results
TSNEPlot(pbmc_small)

# }