plot_mds_pca_2d: MDS or PCA plot (2D)

In szymczak-lab/QCnormSE: Quality Control of Normalized Gene Expression Data

MDS or PCA plot (2D)

Description

Plots two specified components estimated with the function calculate_mds_pca. Color and shape of each sample can be set based on different variables.

Usage

plot_mds_pca_2d(
  res,
  dim = c(1, 2),
  se,
  var.color = NULL,
  palette = NULL,
  var.shape = NULL,
  shape.values = NULL,
  title = NULL,
  factor = 5,
  ellipse = FALSE,
  ellipse.type = "convex"
)

Arguments

res	List. Output of calculate_mds_pca. calculate_mds_pca.
dim	Numeric vector (2). Numbers of components to be plotted.
se	RangedSummarizedExperiment-class object
var.color	Character. Name of variable used to determine color. If NULL black color will be used for all samples.
palette	Color palette to be used (default palette used in ggscatter).
var.shape	Character. Name of variable used to determine shape. If NULL filled circles will be used for all samples.
shape.values	Vector with symbols. Needs to provide a symbol for each unique value of var.shape.
title	Character. Main title of plot.
factor	Numeric. Parameter of the function compute.bagplot. (default: 5)
ellipse	Logical. Should ellipses around points be drawn? (default: FALSE).
ellipse.type	Character. Type of ellipse as given in ggscatter (default: "convex").

Value

List with the following components:

• info: data.frame with information about outlier samples or NULL

• plot: Scatterplot as returned by ggscatter

Examples

data("se.gene")

## PCA
res.pca = calculate_mds_pca(se = se.gene,
                            method = "pca")

## color code by group
plot_mds_pca_2d(res = res.pca,
                dim = c(1, 2),
                se = se.gene,
                var.color = "group")

szymczak-lab/QCnormSE documentation built on March 25, 2023, 1:05 p.m.