run.umap: Run the UMAP algorithm (using umap::umap())

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 UMAP dimensionality reduction algorithm. A UMAP (uniform manifold approximation and projection) 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). For more information on parameter choices, see ?umap::umap.defaults. Uses the R package "umap" to calculate plots and "data.table" to handle data.

Usage

run.umap(dat, use.cols, umap.x.name = "UMAP_X", umap.y.name = "UMAP_Y", umap.seed = 42, neighbours = 15, n_components = 2, metric = "euclidean", n_epochs = 200, input = "data", init = "spectral", min_dist = 0.1, set_op_mix_ratio = 1, local_connectivity = 1, bandwidth = 1, alpha = 1, gamma = 1, negative_sample_rate = 5, a_gradient = NA, b_gradient = NA, spread = 1, transform_state = 42, knn.repeats = 1, verbose = TRUE, umap_learn_args = NA)

Arguments

dat	NO DEFAULT. Input data.table or data.frame.
use.cols	NO DEFAULT. Vector of column names or numbers for clustering.
umap.x.name	DEFAULT = "UMAP_X". Character. Name of UMAP x-axis.
umap.y.name	DEFAULT = "UMAP_Y". Character. Name of UMAP y-axis.
umap.seed	DEFAULT = 42. Numeric. Seed value for reproducibility.
neighbours	DEFAULT = 15. Numeric. Number of nearest neighbours.
n_components	DEFAULT = 2. Numeric. Number of dimensions for output results.
metric	DEFAULT = "euclidean". Character or function. Determines how distances between data points are computed. Can also be "manhattan".
n_epochs	DEFAULT = 200. Numeric. Number of iterations performed during layout optimisation.
input	DEFAULT = "data". Character. Determines whether primary input argument is a data or distance matrix. Can also be "dist".
init	DEFAULT = "spectral". Character or matrix. Deafult "spectral" computes an initial embedding using eigenvectors of the connectivity graph matrix. Can also use "random" (creates an initial layout based on random coordinates).
min_dist	DEFAULT = 0.1. Numeric. Determines how close points appear in final layout.
set_op_mix_ratio	DEFAULT = 1. Numeric in range 0,1. Determines who the knn-graph is used to create a fuzzy simplicial graph.
local_connectivity	DEFAULT = 1. Numeric. Used during construction of fuzzy simplicial set.
bandwidth	DEFAULT = 1. Numeric. Used during construction of fuzzy simplicial set.
alpha	DEFAULT = 1. Numeric. Initial value of "learning rate" of layout optimisation.
gamma	DEFAULT = 1. Numeric. Together with alpha, it determines the learning rate of layout optimisation.
negative_sample_rate	DEFAULT = 5. Numeric. Determines how many non-neighbour points are used per point and per iteration during layout optimisation.
a_gradient	DEFAULT = NA. Numeric. Contributes to gradient calculations during layout optimisation. When left at NA, a suitable value will be estimated automatically.
b_gradient	DEFAULT = NA. Numeric. Contributes to gradient calculations during layout optimisation. When left at NA, a suitable value will be estimated automatically.
spread	DEFAULT = 1. Numeric. Used during automatic estimation of a_gradient/b_gradient parameters.
transform_state	DEFAULT = 42. Numeric. Seed for random number generation used during predict().
knn.repeats	DEFAULT = 1. Numeric. Number of times to restart knn search.
verbose	DEFAULT = TRUE. Logical. Determines whether to show progress messages.
umap_learn_args	DEFAULT = NA. Vector. Vector of arguments to python package umap-learn.

Author(s)

Thomas Ashhurst, [e-mail] Felix Marsh-Wakefield, [e-mail]

Examples

# Run UMAP 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.umap(dat = cell.dat,
                              use.cols = names(demo.asinh)[c(2:10)])

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