plot_umap: UMAP Plot

In dswatson/bioplotr: Pretty, simple, optionally interactive plots for bioinformatics analysis pipelines

UMAP Plot

Description

This function plots a low-dimensional projection of an omic data matrix using the uniform manifold approximation and projection algorithm.

Usage

plot_umap(
  dat,
  group = NULL,
  covar = NULL,
  dist = "euclidean",
  p = 2L,
  top = NULL,
  filter_method = "pairwise",
  center = FALSE,
  dims = c(1L, 2L),
  n_neighbors = 15L,
  label = FALSE,
  pal_group = "npg",
  pal_covar = "Blues",
  size = NULL,
  alpha = NULL,
  title = "UMAP",
  legend = "right",
  hover = FALSE,
  D3 = FALSE,
  ...
)

Arguments

dat	Omic data matrix or matrix-like object with rows corresponding to probes and columns to samples. It is strongly recommended that data be filtered and normalized prior to plotting. Raw counts stored in DGEList or DESeqDataSet objects are automatically extracted and transformed to the log2-CPM scale, with a warning.
group	Optional character or factor vector of length equal to sample size, or up to two such vectors organized into a list or data frame. Supply legend title(s) by passing a named list or data frame.
covar	Optional continuous covariate. If non-NULL, then plot can render at most one group variable. Supply legend title by passing a named list or data frame.
dist	Distance measure to be used. Supports all methods available in dist, Rfast::Dist, and vegdist, as well as those implemented in the bioDist package. See Details.
p	Power of the Minkowski distance.
top	Optional number (if > 1) or proportion (if < 1) of top probes to be used for UMAP.
filter_method	String specifying whether to apply a "pairwise" or "common" filter if top is non-NULL.
center	Center each probe prior to computing distances?
dims	Vector specifying which dimensions to plot. Must be of length two unless D3 = TRUE.
n_neighbors	Number of nearest neighbors for the UMAP algorithm. See umap.defaults for details.
label	Label data points by sample name? Defaults to FALSE unless group and covar are both NULL. If TRUE, then plot can render at most one phenotypic feature.
pal_group	String specifying the color palette to use if group is non-NULL, or a vector of such strings with length equal to the number of vectors passed to group. Options include "ggplot", all qualitative color schemes available in RColorBrewer, and the complete collection of ggsci palettes. Alternatively, a character vector of colors with length equal to the cumulative number of levels in group.
pal_covar	String specifying the color palette to use if covar is non-NULL, or a vector of such strings with length equal to the number of vectors passed to covar. Options include the complete collection of viridis palettes, as well as all sequential color schemes available in RColorBrewer. Alternatively, a character vector of colors representing a smooth gradient, or a list of such vectors with length equal to the number of continuous variables to visualize.
size	Point size.
alpha	Point transparency.
title	Optional plot title.
legend	Legend position. Must be one of "bottom", "left", "top", "right", "bottomright", "bottomleft", "topleft", or "topright".
hover	Show sample name by hovering mouse over data point? If TRUE, the plot is rendered in HTML and will either open in your browser's graphic display or appear in the RStudio viewer.
D3	Render plot in three dimensions?
...	Additional arguments to be passed to umap.

Details

UMAP is a novel machine learning method for visualizing high-dimensional datasets. It is designed to preserve local structure and aids in revealing unsupervised clusters. UMAP is constructed from a theoretical framework based on Riemannian geometry and algebraic topology. See McInnes et al., 2018 for details.

The umap function can operate directly on a distance matrix. Available distance measures include: "euclidean", "maximum", "manhattan", "canberra", "minkowski", "cosine", "pearson", "kendall", "spearman", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao", "mahalanobis", "MI", or "KLD". Some distance measures are unsuitable for certain types of data. See dist_mat for more details on these methods and links to documentation on each.

The top argument optionally filters data using either probewise variance (if filter_method = "common") or the leading fold change method of Smyth et al. (if filter_method = "pairwise"). See plot_mds for more details.

References

McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint: 1802.03426v2.

See Also

umap, plot_pca, plot_mds

Examples

mat <- matrix(rnorm(1000 * 20), nrow = 1000, ncol = 20)
plot_umap(mat)

library(DESeq2)
dds <- makeExampleDESeqDataSet(m = 20)
dds <- rlog(dds)
plot_umap(dds, group = colData(dds)$condition)

dswatson/bioplotr documentation built on March 3, 2023, 9:43 p.m.