Skip to contents

Plot a principal component analysis of the samples

Source: R/plot_mds.R
plot_pca.Rd

Plots the samples on two principal components of a principal component analysis (PCA) of the log2 normalized counts, with the normalization chosen in normalize_data (see plot_explained_variance for the variance explained by each component). Replicates should cluster together and the groups should separate. Unlike plot_mds, which uses the most variable barcodes between each pair of samples, it uses all the barcodes.

Usage

plot_pca(
  screenR_Object,
  groups = NULL,
  components = c(1, 2),
  alpha = 0.8,
  size = 2.5,
  color = "black",
  palette = NULL
)

Arguments

screenR_Object

The ScreenR object obtained using create_screenr_object.

groups

What fills the labels: the name of a sample variable (for example "Treatment" or "Day", see get_sample_table), or a vector with one value for each sample. By default the groups of the ScreenR object (Group).

components

The two principal components to plot. Default c(1, 2).

alpha

The opacity of the labels, between 0 and 1. Default 0.8.

size

The size of the labels. Default 2.5.

color

The color of the label text. Default "black".

palette

An optional vector of colors for the values of groups, which replaces the palette of the object. By default, when groups is the name of a sample variable (or NULL, for Group), the palette of the object for that variable is used when it is set (see get_palettes), otherwise the default colors of ggplot2.

Value

A ggplot object.

See also

plot_explained_variance, plot_mds

Examples

object <- get0("object", envir = asNamespace("ScreenR"))

plot_pca(object)


# Second and third components
plot_pca(object, components = c(2, 3))