PTXQC (version 1.1.2)

getPCA: Create a principal component analysis (PCA) plot for the first two dimensions.

Description

Create a principal component analysis (PCA) plot for the first two dimensions.

Usage

getPCA(data, do_plot = TRUE, connect_line_order = NA, gg_layer)

Value

[invisible] Named list with "PCA": The PCA object as returned by prcomp, access $x for PC values and "plots": list of plot objects (one or two)

Arguments

data

Matrix(!) where each row is one high-dimensional point, with ncol dimensions, e.g. a mouse as an array of proteinexpressions rownames(data) give classes for colouring (can be duplicates in matrices, as opposed to data.frames)

do_plot

Show PCA plot? if ==2, then shows correlations plot as well

connect_line_order

Connect points by lines, the order is given by this vector. Default: NA (no lines)

gg_layer

More parameters added to a ggplot object (ggplot(x) + gg_layer)

Examples

n = 5
m = 10
data = matrix(runif(n * m), nrow = n, ncol = m)
rownames(data) = 1:n
getPCA(data, connect_line_order = 1:n, gg_layer = ggplot2::ggtitle("test"))