Create a principal component analysis (PCA) plot for the first two dimensions.
getPCA(data, do_plot = TRUE, connect_line_order = NA, gg_layer)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)
Show PCA plot? if ==2, then shows correlations plot as well
Connect points by lines, the order is given by this vector. Default: NA (no lines)
More parameters added to a ggplot object (ggplot(x) + gg_layer)
[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)