plot_arc: Plot data with archetypes in 2D, 3D and a panel of 2D

In vitkl/ParetoTI: R toolbox for Archetypal Analysis and Pareto Task Inference on single cell data

Description

plot_arc() plot data with polytope representing the Pareto front, where vertices are archetypes (dots connected with lines). When archetype data is "b_pch_fit" all archetype locations from each subsample are shown with lines connecting the average location (type "average"); or lines connecting archetypes in each of the experiments (colored differently, type "all").

arch_to_tsne() Project archetype positions to tSNE coordinates (2D or 3D) using Rtsne.

arch_to_umap() Project archetype positions to UMAP coordinates using umap.

Usage

plot_arc(arc_data = NULL, data, which_dimensions = as.integer(1:2),
  type = c("average", "all")[1], average_func = mean,
  geom = list(ggplot2::geom_point, ggplot2::geom_bin2d)[[1]],
  colors = c("#1F77B4", "#D62728", "#2CA02C", "#17BED0", "#006400",
  "#FF7E0F"), arch_size = NULL, arch_alpha = 0.4, data_size = 4,
  data_alpha = 1, line_size = NULL, data_lab = "data",
  arc_lab = "archetypes", arc_names_num = TRUE, legend_name = "data",
  text_size = NULL, nudge = c(0.05, 0.1))

arch_to_tsne(arc_data, data, which_dimensions = 1:2, pca = FALSE,
  partial_pca = FALSE, ...)

arch_to_umap(arc_data, data, which_dimensions = 1:2,
  method = c("naive", "umap-learn")[1], n_neighbors = 30L,
  min_dist = 0.3, metric = ifelse(method[1] == "umap-learn",
  "correlation", "euclidean"), ...)

Arguments

arc_data	objects of class "pch_fit", "b_pch_fit", "k_pch_fit" storing the position of archetypes, and other data from fit_pch() run. arc_data$XC is matrix of dim(dimensions, archetypes) or list where each element is XC matrix from an independent run of the archetypal analysis. Set to NULL if you want to show data alone.
data	matrix of data in which archetypes/polytope were found, dim(variables/dimentions, examples)
which_dimensions	indices or character vector specifying dimension names. 2D plot, 3D plot or a panel for 2D plots when more than 3 dimensions. For arch_to_tsne() use 1:2 or 1:3. When which_dimensions exceeds the number of dimensions in arc_data these archetypes will be omitted. This can happen when fitting simplexes: lines and triangles are only 2D, so will be omitted from 3D plots.
type	used when arc_data is "b_pch_fit", one of "average", "all"
average_func	used when arc_data is "b_pch_fit", function telling how to find average position of vertices
geom	plotting function to plot data in 2D, useful options are ggplot2::geom_point (scatterplot) and ggplot2::geom_bin2d (density)
colors	character vector giving color palette for different archetype fits and the data (both 3D and 2D plot)
arch_size	size of archetype points
arch_alpha	opacity of archetype points
data_size	size of data points in plotly. Values for ggplot are 1/2 of data_size.
data_alpha	opacity of data points
line_size	width of lines connecting archetypes
data_lab	vector, 1L or length of data, label data points (examples) with a qualitative or quantitative label
arc_lab	vector, 1L or nrow(arc_data$XC)/noc, label vertices/archetypes (points) with a categorical. Only used when looking at a single fit (pch_fit).
arc_names_num	logical, when archetypes are named, use numbers (default, TRUE), or names (FALSE, produces cluttered plot)?
legend_name	name to display on legend, e.g. gene name in data_lab
text_size	archetype label text size
pca	perform PCA? Argument to Rtsne.
partial_pca	perform partial PCA? Argument to Rtsne.
...	additional arguments to Rtsne and umap.
method	Method for finding UMAP representation. Available methods are 'naive' (an implementation written in pure R) and 'umap-learn' (requires python package 'umap-learn'). See umap for details.
n_neighbors	sensible default for umap, pass other parameters via ...
min_dist	sensible default for umap
metric	sensible default for umap

Value

plot_arc() ggplot2 (2D) or plotly (3D) plot

arch_to_tsne() list with: arc_data containing archetype positions in tSNE coordinates, and data positions in tSNE coordinates

arch_to_umap() list with: arc_data containing archetype positions in UMAP coordinates, data positions in UMAP coordinates, and umap_config parameters used to find this representation.

Author(s)

Vitalii Kleshchevnikov

See Also

fit_pch, arch_dist

Examples

library(ParetoTI)
library(ggplot2)
# Random data that fits into the triangle (2D)
set.seed(4355)
archetypes = generate_arc(arc_coord = list(c(5, 0), c(-10, 15), c(-30, -20)),
                          mean = 0, sd = 1)
data = generate_data(archetypes$XC, N_examples = 1e4, jiiter = 0.04, size = 0.9)
plot_arc(arc_data = archetypes, data = data,
    which_dimensions = 1:2) +
    theme_bw()
# Plot data as 2D density rather than points
plot_arc(arc_data = archetypes, data = data,
    which_dimensions = 1:2, geom = ggplot2::geom_bin2d)

# Random data that fits into the triangle (3D)
set.seed(4355)
archetypes = generate_arc(arc_coord = list(c(5, 0, 4), c(-10, 15, 0), c(-30, -20, -5)),
                          mean = 0, sd = 1)
data = generate_data(archetypes$XC, N_examples = 1e4, jiiter = 0.04, size = 0.9)

plot_arc(arc_data = archetypes, data = data,
    which_dimensions = 1:3)

# Project to tSNE coordinates (from 3D to 2D)
arc_tsne = arch_to_tsne(archetypes, data, which_dimensions = 1:2)
plot_arc(arc_data = arc_tsne$arc_data, data = arc_tsne$data,
    which_dimensions = 1:2) +
    theme_bw()

# Project to UMAP representation
arc_umap = arch_to_umap(archetypes, data, which_dimensions = 1:2,
                        method = c("naive", # implemented in R and slow
                                   "umap-learn")) # requires python module
plot_arc(arc_data = arc_umap$arc_data, data = arc_umap$data,
    which_dimensions = 1:2) +
    theme_bw()

vitkl/ParetoTI documentation built on Feb. 11, 2020, 1:36 a.m.