Add a TSNE embedding of a reduced dimensions object to an ArchRProject

This function will compute a TSNE embedding and add it to an ArchRProject.

addTSNE(
  ArchRProj = NULL,
  reducedDims = "IterativeLSI",
  method = "RTSNE",
  name = "TSNE",
  perplexity = 50,
  maxIterations = 1000,
  learningRate = 200,
  dimsToUse = NULL,
  scaleDims = NULL,
  corCutOff = 0.75,
  saveModel = FALSE,
  verbose = TRUE,
  seed = 1,
  force = FALSE,
  threads = max(floor(getArchRThreads()/2), 1),
  ...
)

Arguments

ArchRProj	An `ArchRProject` object.
reducedDims	The name of the `reducedDims` object (i.e. "IterativeLSI") to use from the designated `ArchRProject`.
method	The method for computing a TSNE embedding to add to the `ArchRProject` object. Possible options are "RTSNE", which uses `Rtsne::Rtsne()`, and "FFRTSNE", which uses `Seurat::RunTSNE()`.
name	The name for the TSNE embedding to store in the given `ArchRProject` object.
perplexity	An integer describing the number of nearest neighbors to compute an `Rtsne`. This argument is passed to `perplexity` in `Rtsne::Rtsne()`.
maxIterations	An integer describing the maximum number of iterations when computing a TSNE. This argument is passed to `max_iter` in `Rtsne::Rtsne()`.
learningRate	An integer controlling how much the weights are adjusted at each iteration. This argument is passed to `eta` in `Rtsne::Rtsne()`.
dimsToUse	A vector containing the dimensions from the `reducedDims` object to use in computing the embedding.
scaleDims	A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to `NULL` this will scale the dimensions based on the value of `scaleDims` when the `reducedDims` were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.
corCutOff	A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the `corCutOff`, it will be excluded from analysis.
verbose	A boolean value that indicates whether printing TSNE output.
seed	A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.
force	A boolean value that indicates whether to overwrite the relevant data in the `ArchRProject` object if the embedding indicated by `name` already exists.
threads	The number of threads to be used for parallel computing.
...	Additional parameters for computing the TSNE embedding to pass to `Rtsne::Rtsne()` (when `method = "RTSNE"`) or to `Seurat::RunTSNE()` (when method = "FFRTSNE").