run.tsne: Run the tSNE algorithm (using Rtsne::Rtsne())

In sydneycytometry/Spectre: A computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry data.

Description

Method to run a tSNE dimensionality reduction algorithm. A tSNE (t-distributed stochastic neighbor embedding) plot is a useful means to visualise data. As it is a dimensionality reduction algorithm, some data will be lost. It is good practice to validate any populations (namely through manual gating). Output data will be "tsne.res". Uses the R package "Rtsne" to calculate plots.

Usage

run.tsne(dat, use.cols, tsne.x.name, tsne.y.name, tsne.seed, dims, initial_dims, perplexity, theta, check_duplicates, pca, max_iter, verbose, is_distance, Y_init, stop_lying_iter, mom_switch_iter, momentum, final_momentum, eta, exaggeration_factor)

Arguments

dat	NO DEFAULT. data.frame.
use.cols	NO DEFAULT. Vector of numbers, reflecting the columns to use for dimensionality reduction.
tsne.x.name	DEFAULT = "tSNE_X". Character. Name of tSNE x-axis.
tsne.y.name	DEFAULT = "tSNE_Y". Character. Name of tSNE y-axis.
tsne.seed	DEFAULT = 42. Numeric. Seed value for reproducibility.
dims	DEFAULT = 2. Number of dimensions for output results, either 2 or 3.
initial_dims	DEFAULT = 50. Number of dimensions retained in initial PCA step.
perplexity	DEFAULT = 30.
theta	DEFAULT = 0.5. Use 0.5 for Barnes-Hut tSNE, 0.0 for exact tSNE (takes longer).
check_duplicates	DEFAULT = FALSE.
pca	DEFAULT = TRUE. Runs PCA prior to tSNE run.
max_iter	DEFAULT = 1000. Maximum number of iterations.
verbose	DEFAULT = TRUE.
is_distance	DEFAULT = FALSE. Experimental, using X as a distance matrix.
Y_init	DEFAULT = NULL. Recommend NULL for random initialisation.
stop_lying_iter	DEFAULT = 250. Number of iterations of early exaggeration.
mom_switch_iter	DEFAULT = 250. Number of iterations before increased momentum of spread.
momentum	DEFAULT = 0.5. Initial momentum of spread.
final_momentum	DEFAULT = 0.8. Momentum of spread at 'final_momentum'.
eta	DEFAULT = 200. Learning rate.
exaggeration_factor	DEFAULT = 12.0. Factor used during early exaggeration.

Author(s)

Felix Marsh-Wakefield, [e-mail]

Examples

# Run tSNE on a subset of the  demonstration dataset

cell.dat <- do.subsample(Spectre::demo.asinh, 10000) # Subsample the demo dataset to 10000 cells
cell.dat <- Spectre::run.tsne(dat = cell.dat,
                              use.cols = names(demo.asinh)[c(2:10)])

sydneycytometry/Spectre documentation built on March 20, 2021, 2:15 a.m.