Project archetypes and data in PCA dimentions

project_to_pcs() projects archetypes (arc_data) and data points (arc_data) to PC space. Archetypes are projected into PC space of data, e.i. archetypes do not affect PCA and are just projected afterwards.

project_from_pc() projects archetypes and data points to original space provided SVD decomposition results. Optionally do the reverse of log2 transformation to obtain normalised expression space.

project_to_pcs(arc_data = NULL, data, n_dim = nrow(data), s = NULL,
  pc_method = c("svd", "irlba"), log2 = FALSE, offset = 1,
  zscore = FALSE)

project_from_pc(arc_data, s, undo_zscore = FALSE, undo_log2 = FALSE,
  offset = 1)

Arguments

arc_data	objects of class "pch_fit", "b_pch_fit" storing the position of archetypes and other data produced by `fit_pch`(). arc_data$XC is matrix of dim(dimensions, archetypes) or list where each element is XC matrix from an independent run of the archetypal analysis.
data	matrix of data used in archetypal analysis, dim(variables/dimentions, examples)
n_dim	number of principal component dimensions
s	list 's' containing SVD decomposition results (U, d, Vt), standard deviation and mean of genes used for decomposition (sd, means)
pc_method	method to use for finding PCs: `svd` or `irlba`
log2	log2-transform before to z-scoring and PC-projection
offset	log2 transformation offset (e.g. `log2(x + offset)`)
zscore	standardise (substract the mean and divide by standard deviation) prior to PC-projection
undo_zscore	undo z-scoring by multiplying by standard deviation and adding the mean? Undo z-scoring precedes exponentiation.
undo_log2	undo log2-transformation by exponentiating and substracting pseudocount?

Value

project_to_pcs(): list with projected $data, archetypes ($arc_data) and $s list of decomposition matrices, sds and means

project_from_pc(): archetypes projected to data space

Examples

# Random data that fits into the triangle
set.seed(4355)
arc_data = generate_arc(arc_coord = list(c(5, 0), c(-10, 15), c(-30, -20)),
                          mean = 0, sd = 1)
data = generate_data(arc_data$XC, N_examples = 1e4, jiiter = 0.04, size = 0.9)
# Plot
plot_arc(arc_data = arc_data, data = data,
         which_dimensions = 1:2, data_alpha = 0.5) +
         ggplot2::theme_bw()

# Project to PCs (in this case just rotate to align x-axis with
#                  the axis of most variation because the data is already 2D)
pcs = project_to_pcs(arc_data, data, n_dim = 2, pc_method = c("svd", "irlba")[1])
# Plot in PC coordinates
plot_arc(arc_data = pcs$arc_data, data = pcs$data,
         which_dimensions = 1:2, data_alpha = 0.5) +
         ggplot2::theme_bw()

# Project from PCs back to expression
projected = project_from_pc(pcs$arc_data, pcs$s,
                            undo_zscore = FALSE, undo_log2 = FALSE)

# Plot plot in projected coordinates
plot_arc(arc_data = projected, data = data,
         which_dimensions = 1:2, data_alpha = 0.5) +
         ggplot2::theme_bw()

Arguments

Value

Examples

Contents