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PCA scatter plot

Source: R/scatter.R
plot_pca.Rd

Computes PCA using one of the methods provided in the Bioconductor package pcaMethods and plots the two first principal components.

Usage

plot_pca(
  object,
  pcs = c(1, 2),
  all_features = FALSE,
  center = TRUE,
  scale = "uv",
  color = NULL,
  shape = color,
  label = NULL,
  density = FALSE,
  title = "PCA",
  subtitle = NULL,
  color_scale = NA,
  shape_scale = getOption("notame.shape_scale"),
  fill_scale = getOption("notame.fill_scale_dis"),
  text_base_size = 14,
  point_size = 2,
  assay.type = NULL,
  ...
)

Arguments

object

a SummarizedExperiment or MetaboSet object

pcs

numeric vector of length 2, the principal components to plot

all_features

logical, should all features be used? If FALSE (the default), flagged features are removed before visualization.

center

logical, should the data be centered prior to PCA? (usually yes)

scale

scaling used, as in prep. Default is "uv" for unit variance

color

character, name of the column used for coloring the points. Set to NULL for black color.

shape

character, name of the column used for shape. Set to NULL for uniform round shapes.

label

character, name of the column used for point labels

density

logical, whether to include density plots to both axes. The density curves will be split and colored by the 'color' variable.

title, subtitle

the titles of the plot

color_scale

the color scale as returned by a ggplot function. Set to NA to choose the appropriate scale based on the class of the coloring variable.

shape_scale

the shape scale as returned by a ggplot function

fill_scale

the fill scale used for density curves. If a continuous variable is used as color, density curve will be colorless.

text_base_size

numeric, base size for text

point_size

numeric, size of the points

assay.type

character, assay to be used in case of multiple assays

...

additional arguments passed to pca

Value

A ggplot object. If density is TRUE, the plot will consist of multiple parts and is harder to modify.

See also

pca

Examples

data(example_set)
plot_pca(example_set, color = "Injection_order", shape = "Group")
#> svd calculated PCA
#> Importance of component(s):
#>                  PC1     PC2
#> R2            0.2504 0.05641
#> Cumulative R2 0.2504 0.30681