plot_pca_loadings: PCA loadings plot

In antonvsdata/notame: Workflow for non-targeted LC-MS metabolic profiling

PCA loadings plot

Description

Computes PCA using one of the methods provided in the Bioconductor package pcaMethods and plots the loadings of first principal components CITATION: When using this function, cite the pcaMethods package

Usage

plot_pca_loadings(
  object,
  pcs = c(1, 2),
  all_features = FALSE,
  center = TRUE,
  scale = "uv",
  n_features = c(10, 10),
  title = "PCA loadings",
  subtitle = NULL,
  text_base_size = 14,
  point_size = 2,
  label_text_size = 4,
  ...
)

Arguments

object	a 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 pcaMethods::prep. Default is "uv" for unit variance
n_features	numeric vector of length two, number of top feature to plot for each principal component
title, subtitle	the titles of the plot
text_base_size	numeric, base size for text
point_size	numeric, size of the points
label_text_size	numeric, size of the labels
...	additional arguments passed to pcaMethods::pca

Value

a ggplot object.

See Also

pca

Examples

plot_pca_loadings(merged_sample, n_features = c(2, 4))

antonvsdata/notame documentation built on Sept. 14, 2024, 11:09 p.m.