| runTSNE | R Documentation | 
Runs t-SNE on the aligned cell factors (result from
alignFactors), or unaligned cell factors (result from
runIntegration)) to generate a 2D embedding for visualization.
By default Rtsne (Barnes-Hut implementation of t-SNE)
method is invoked, while alternative "fftRtsne" method (FFT-accelerated
Interpolation-based t-SNE, using Kluger Lab implementation) is also
supported. For very large datasets, it is recommended to use
method = "fftRtsne" due to its efficiency and scalability.
Extra external installation steps are required for using "fftRtsne" method. Please consult detailed guide.
runTSNE(
  object,
  useRaw = NULL,
  useDims = NULL,
  nDims = 2,
  usePCA = FALSE,
  perplexity = 30,
  theta = 0.5,
  method = c("Rtsne", "fftRtsne"),
  dimredName = "TSNE",
  asDefault = NULL,
  fitsnePath = NULL,
  seed = 42,
  verbose = getOption("ligerVerbose", TRUE),
  k = nDims,
  use.raw = useRaw,
  dims.use = useDims,
  use.pca = usePCA,
  fitsne.path = fitsnePath,
  rand.seed = seed
)
| object | liger object with factorization results. | 
| useRaw | Whether to use un-aligned cell factor loadings ( | 
| useDims | Index of factors to use for computing the embedding. Default
 | 
| nDims | Number of dimensions to reduce to. Default  | 
| usePCA | Whether to perform initial PCA step for Rtsne. Default
 | 
| perplexity | Numeric parameter to pass to Rtsne (expected number of
neighbors). Default  | 
| theta | Speed/accuracy trade-off (increase for less accuracy), set to
 | 
| method | Choose from  | 
| dimredName | Name of the variable in  | 
| asDefault | Logical, whether to set the resulting dimRed as default for
visualization. Default  | 
| fitsnePath | Path to the cloned FIt-SNE directory (i.e.
 | 
| seed | Random seed for reproducibility. Default  | 
| verbose | Logical. Whether to show information of the progress. Default
 | 
| use.raw,dims.use,k,use.pca,fitsne.path,rand.seed | Deprecated. See Usage section for replacement. | 
The object where a "TSNE" variable is updated in the
cellMeta slot with the whole 2D embedding matrix.
runUMAP
pbmc <- runTSNE(pbmcPlot)
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