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Create a Principle Component plot from the feature quantification

plot_pca.Rd

A PCA visualisation of feature quantifications in each sample can allow one to see how the experimental conditions relate to the sources of variance (principle components). This function plots a PCA, with the option to colour and/or shape the points by experimental conditions. The percentage values indicated on the axes are the variance explained by the PCs.

Usage

plot_pca(
  obj,
  i,
  allowing_missing = FALSE,
  colour_by = NULL,
  shape_by = NULL,
  x = 1,
  y = 2,
  ...
)

Arguments

obj

QFeatures. Proteomics dataset

i

string. Index for the SummarizedExperiment you wish to plot

allowing_missing

logical. If TRUE, will use pcaMethods::pca to allow for missing values. If FALSE (default), will use stats::prcomp and remove any features with missing values

colour_by

string. ColData column to colour points by

shape_by

string. ColData column to shape points by

x

numeric. Principle component to plot on x-axis

y

numeric. Principle component to plot on x-axis

Value

Returns a ggplot object.