RunDiffusion: Run t-distributed Stochastic Neighbor Embedding

In nukappa/seurat_v2: Seurat : R toolkit for single cell genomics

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

Usage

RunDiffusion(object, cells.use = NULL, dims.use = 1:5, k.seed = 1,
  do.fast = FALSE, add.iter = 0, genes.use = NULL,
  reduction.use = "pca", dim_embed = 2, q.use = 0.05, max.dim = 2,
  scale.clip = 3, ...)

Arguments

object	Seurat object
cells.use	Which cells to analyze (default, all cells)
dims.use	Which dimensions to use as input features
k.seed	Random seed for the t-SNE
do.fast	If TRUE, uses the Barnes-hut implementation, which runs faster, but is less flexible
add.iter	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
genes.use	If set, run the tSNE on this subset of genes (instead of running on a set of reduced dimensions). Not set (NULL) by default
reduction.use	Which dimensional reduction (PCA or ICA) to use for the tSNE. Default is PCA
dim_embed	The dimensional space of the resulting tSNE embedding (default is 2). For example, set to 3 for a 3d tSNE
q.use	Quantile to use
max.dim	Max dimension to keep from diffusion calculation
scale.clip	Max/min value for scaled data. Default is 3
...	Additional arguments to the tSNE call. Most commonly used is perplexity (expected number of neighbors default is 30)

Value

Returns a Seurat object with a tSNE embedding in object@tsne_rot

nukappa/seurat_v2 documentation built on May 24, 2019, 9:57 a.m.