PCA hexbin plot

Computes PCA using one of the methods provided in the Bioconductor package pcaMethods and plots the two first principal components as hexagonal bins, where the value of the coloring variable is summarised for each bin, by default as the mean of the values inside the bin.

Usage

plot_pca_hexbin(
  object,
  pcs = c(1, 2),
  all_features = FALSE,
  center = TRUE,
  scale = "uv",
  fill = "Injection_order",
  summary_fun = "mean",
  bins = 10,
  title = "PCA",
  subtitle = NULL,
  fill_scale = getOption("notame.fill_scale_con"),
  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

fill

character, name of the column used for coloring the hexagons

summary_fun

the function used to compute the value for each hexagon

bins

the number of bins in x and y axes

title, subtitle

the titles of the plot

fill_scale

the fill scale as returned by a ggplot function

assay.type

character, assay to be used in case of multiple assays

...

additional arguments passed to pca

Value

A ggplot object.

See also

pca

Examples

data(example_set)
plot_pca_hexbin(example_set)
#> svd calculated PCA
#> Importance of component(s):
#>                  PC1     PC2
#> R2            0.2504 0.05641
#> Cumulative R2 0.2504 0.30681
#> Warning: Computation failed in `stat_summary_hex()`.
#> Caused by error in `compute_group()`:
#> ! The package "hexbin" is required for `stat_summary_hex()`.