Seurat: RunTSNE – R documentation

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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 class 'matrix'
RunTSNE(
  object,
  assay = NULL,
  seed.use = 1,
  tsne.method = "Rtsne",
  dim.embed = 2,
  reduction.key = "tSNE_",
  ...
)

## S3 method for class 'DimReduc'
RunTSNE(
  object,
  cells = NULL,
  dims = 1:5,
  seed.use = 1,
  tsne.method = "Rtsne",
  dim.embed = 2,
  reduction.key = "tSNE_",
  ...
)

## S3 method for class 'dist'
RunTSNE(
  object,
  assay = NULL,
  seed.use = 1,
  tsne.method = "Rtsne",
  dim.embed = 2,
  reduction.key = "tSNE_",
  ...
)

## S3 method for class 'Seurat'
RunTSNE(
  object,
  reduction = "pca",
  cells = NULL,
  dims = 1:5,
  features = NULL,
  seed.use = 1,
  tsne.method = "Rtsne",
  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. If NULL, does not set the seed
`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
`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

Seurat

Tools for Single Cell Genomics

v4.0.1

GPL-3 | file LICENSE

Authors

Andrew Butler [ctb] (<https://orcid.org/0000-0003-3608-0463>), Saket Choudhary [ctb] (<https://orcid.org/0000-0001-5202-7633>), Charlotte Darby [ctb] (<https://orcid.org/0000-0003-2195-5300>), Jeff Farrell [ctb], Christoph Hafemeister [ctb] (<https://orcid.org/0000-0001-6365-8254>), Yuhan Hao [ctb] (<https://orcid.org/0000-0002-1810-0822>), Paul Hoffman [aut, cre] (<https://orcid.org/0000-0002-7693-8957>), Jaison Jain [ctb] (<https://orcid.org/0000-0002-9478-5018>), Efthymia Papalexi [ctb] (<https://orcid.org/0000-0001-5898-694X>), Patrick Roelli [ctb], Rahul Satija [ctb] (<https://orcid.org/0000-0001-9448-8833>), Karthik Shekhar [ctb], Avi Srivastava [ctb] (<https://orcid.org/0000-0001-9798-2079>), Tim Stuart [ctb] (<https://orcid.org/0000-0002-3044-0897>), Kristof Torkenczy [ctb] (<https://orcid.org/0000-0002-4869-7957>), Shiwei Zheng [ctb] (<https://orcid.org/0000-0001-6682-6743>), Satija Lab and Collaborators [fnd]

Initial release

2021-03-17

RunTSNE

Description

Usage

Arguments

Seurat

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