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.
RunTSNE(object, reduction.use = "pca", cells.use = NULL, dims.use = 1:5,
genes.use = NULL, seed.use = 1, do.fast = TRUE, add.iter = 0,
dim.embed = 2, distance.matrix = NULL, ...)Seurat object
Which dimensional reduction (e.g. PCA, ICA) to use for the tSNE. Default is PCA
Which cells to analyze (default, all cells)
Which dimensions to use as input features
If set, run the tSNE on this subset of genes (instead of running on a set of reduced dimensions). Not set (NULL) by default
Random seed for the t-SNE
If TRUE, uses the Barnes-hut implementation, which runs faster, but is less flexible. TRUE by default.
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
The dimensional space of the resulting tSNE embedding (default is 2). For example, set to 3 for a 3d tSNE
If set, tuns tSNE on the given distance matrix instead of data matrix (experimental)
Additional arguments to the tSNE call. Most commonly used is perplexity (expected number of neighbors default is 30)
Returns a Seurat object with a tSNE embedding in object@dr$tsne@cell.embeddings