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), ... )
The name of the
The name for the TSNE embedding to store in the given
An integer describing the number of nearest neighbors to compute an
An integer describing the maximum number of iterations when computing a TSNE. This argument is passed to
An integer controlling how much the weights are adjusted at each iteration. This argument is passed to
A vector containing the dimensions from the
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
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
A boolean value that indicates whether printing TSNE output.
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.
A boolean value that indicates whether to overwrite the relevant data in the
The number of threads to be used for parallel computing.